Sample container identification

Abstract

The present invention provides a system and method for identifying various sample containers loaded in a rack. The system and method are performed to identify the sample container in the rack and detect various characteristics associated with the container and / or the rack, and the various characteristics are evaluated to determine the validity and / or type of the container in the rack.

Description

[0001] This application was filed as a PCT international patent application on October 26, 2018, and claims priority to U.S. Provisional Patent Application Serial No. 62 / 578,236 filed on October 27, 2017, the entire disclosure of which is incorporated by reference in its entirety. Background Art

[0002] Sample analyzers typically use a sample presentation unit (SPU) to support and transfer a sample rack that holds multiple sample containers such as sample tubes or cups. A typical SPU only allows a single type of container to be held in a single sample rack. Problems can occur when a user accidentally loads another type of sample container into the rack, or when a container in the rack becomes blocked or not loaded into the proper position on the rack. Summary of the invention

[0003] In general, the present disclosure relates to sample container identification. In some embodiments, as a non-limiting example, a sample presentation unit in a sample analyzer is configured to identify a sample container type in a rack.

[0004] In one aspect, an automated clinical analyzer comprises: a sample rack presentation unit configured to present a sample rack to a sample pipette, the sample rack having a plurality of sample container positions; an image capture device coupled to the sample rack presentation unit, the image capture device configured to capture at least one image on at least one region of interest on the sample rack, the at least one region of interest comprising at least one sample container; a computing device configured to identify a sample container type in a captured image by matching the captured image with a reference image, the reference image comprising images of a plurality of different sample container types; and the computing device is further configured to identify the sample container type in the captured image based on a matching threshold with the reference image, wherein if the captured image exceeds or equals the threshold of the reference image, the sample container type is identified as the sample container type of the reference image, and wherein if the captured image does not meet the threshold of any reference image, the sample container type is not identified.

[0005] On the other hand, a method for determining a type of a container in a rack includes: capturing at least one image of at least a portion of a container and at least a portion of a rack using an image capture device, the container being held by the rack; finding a reference feature on the rack as a first image feature in at least one image; at least partially defining a first search area in at least one image based on a first relative position to the first image feature in at least one image; finding a first feature on the container within the first search area in at least one image as a second image feature in at least one image; calculating a first geometric property of the container based on a position of the second image feature in at least one image; and determining the type of the container by looking up the first geometric property of the container in a table.

[0006] On the other hand, a method for determining the type and identity of a container in a rack includes: capturing at least one high exposure image of at least a portion of the container and at least a portion of the rack using an image capture device, the container being held by the rack; reading a code mark from at least one high exposure image to determine the identity of the container in the rack; capturing at least one low exposure image of at least a portion of the container and at least a portion of the rack using an image capture device; finding a reference feature on the rack as an image feature in at least one low exposure image and / or at least one high exposure image; at least partially defining a search area in at least one low exposure image based on a relative position to a first image feature; finding a feature on the container within a first search area in at least one low exposure image as a second image feature; calculating a geometric property of the container based on the position of the second image feature; and determining the type of the container by looking up the geometric property of the container in a table.

[0007] On the other hand, a method for determining whether a lid is on a container in a holder includes: capturing at least one image of at least a portion of a container and at least a portion of a holder using an image capture device, the container being held by the holder; finding a reference feature on the holder as a first image feature in at least one image; at least partially defining a search area in at least one image based on a first relative position to the first image feature in at least one image; finding a feature on the container within the search area in at least one image as a second image feature in at least one image; defining a histogram area based on a second relative position of the second image feature in at least one image; and extracting histogram information from the histogram area to determine whether the lid is on the container. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a top plan view of an exemplary sample analyzer.

[0009] Figure 2 Depicts the installation Figure 1A perspective view of an exemplary bracket at a first position in a sample presentation unit (SPU) of a sample analyzer of FIG.

[0010] Figure 3 Depicted Figure 2 but with the bracket in a second position in the SPU.

[0011] Figure 4 Depicted Figure 2 but wherein the bracket is in a third position in which the bracket is partially moved out of the second position of the SPU.

[0012] Figure 5 is a front view of an exemplary pipe rack.

[0013] Figure 6 yes Figure 5 Perspective view of the pipe rack.

[0014] Figure 7 is a front view of an exemplary cup holder.

[0015] Figure 8 yes Figure 7 A perspective view of a cup holder.

[0016] Fig. 9 is a multi-sample tube with different types Figure 5 A perspective cutaway view of a pipe rack.

[0017] Fig.10 is a perspective view showing an exemplary type of sample cup.

[0018] Fig.11 is a perspective view showing another exemplary type of sample cup.

[0019] Fig.12 is a perspective view showing yet another exemplary type of sample cup.

[0020] Fig.13 is a perspective view showing yet another exemplary type of sample cup.

[0021] Fig.14 is a perspective view of the pipe rack located in the presentation channel of the SPU.

[0022] Fig.15 yes Fig.14 An enlarged portion of a perspective view.

[0023] Fig.16 is another perspective view of the tube rack located in the presentation channel of the SPU, partially based on the camera unit.

[0024] Fig.17 is another perspective view of a tube rack located in a presentation channel of an SPU, in part according to a container inspection unit.

[0025] Fig.18 is a front view showing an exemplary configuration of a camera unit having a mounting bracket.

[0026] Fig.19 is a flow chart of an exemplary method for performing sample container identification relative to a rack.

[0027] Fig. 20 is a schematic diagram showing different image positions of a stent.

[0028] Fig.21A is a low exposure monochrome image of a portion of a tube rack with sample tubes.

[0029] Fig.21B is a low exposure monochrome image of another portion of the tube rack with the sample tubes.

[0030] Fig. 21C is a low exposure monochrome image of another portion of a tube rack having sample tubes including one sample tube with a cap.

[0031] Fig. 22 is a low exposure monochrome image of a portion of a cup holder with a sample cup.

[0032] Fig.23A is a high exposure monochrome image of a portion of a tube rack with sample tubes.

[0033] Fig. 23B is the barcode that identifies the corresponding sample tube Fig.23A Monochrome image.

[0034] Fig.24 is a flow chart of another exemplary method of performing sample container identification relative to a rack.

[0035] Fig.25 is a flow chart of an exemplary method for processing an image of a scaffold having one or more vessels and determining characteristics of the vessels therein.

[0036] Fig.26 The features of the bracket are identified Fig.21A of the image.

[0037] Fig. 27 It is the one that identifies the container characteristics Fig.21A of the image.

[0038] Fig.28 It is the one that identifies the container characteristics Fig.21A of the image.

[0039] Fig.29 The histogram features are identified Fig.21A of the image.

[0040] Fig.30 The histogram features are identified Fig. 21C of the image.

[0041] Fig.31 is an exemplary classification table.

[0042] Fig.32 is a monochrome image of a portion of a cup holder with a sample cup and showing the identified histogram features.

[0043] Fig.33 is a flow chart of an exemplary method of adding and verifying a new container type for use with a sample analyzer.

[0044] Fig.34 An exemplary architecture of a computing device that can be used to implement aspects of the present disclosure is shown. DETAILED DESCRIPTION

[0045] Various embodiments will be described in detail below in conjunction with the accompanying drawings, wherein similar reference numerals represent similar parts and components in several drawings. The reference to various embodiments is not intended to limit the scope of the claims appended hereto. In addition, any examples cited in this specification are not intended to be limiting, but are merely intended to state some of the multiple possible embodiments for the appended claims.

[0046] Figure 1 1 is a top plan view of an exemplary sample analyzer. In this example, the sample analyzer is generally designated as reference numeral 100 and is configured to analyze a sample. The sample analyzer 100 includes a sample holder 102, a sample presentation unit (SPU) 104, a sample pipette transfer unit 106, an analysis unit 108, and a sample container identification unit 110.

[0047] The rack 102 is configured to hold and transfer one or more sample containers 180. For example, the rack 102 can be used in a variety of applications and is configured to transfer one or more containers 180 within or outside the sample analyzer 100. Figures 5 to 8 As shown, one or more sample containers 180 can be positioned in various combinations in the rack 102. As described herein, one or more sample containers 180 can be individually inserted and engaged with the rack 102. Although a single rack 102 is shown in this example, it should be understood that the sample analyzer 100 is configured to support multiple racks 102, which can be used in various combinations in the sample analyzer 100 and operated individually or in any combination.

[0048] The SPU 104 is used to support the rack 102 and transfer the rack 102 to various locations. Figures 2 to 9 An exemplary operation of the bracket 102 is further described and illustrated.

[0049] The sample pipette transfer unit 106 is used to extract samples from the container in the rack 102 and transfer the samples to the analysis unit for analysis. The sample pipette transfer unit 106 includes a disposable tip, a mandrel for holding the tip, a sensor for level sensing fluid, a pump for aspirating fluid and / or dispensing fluid, and a pipe / valve. The function of the pipette is to aspirate fluid from a sample container or vessel and deliver it to other vessels for analysis.

[0050] The analysis unit 108 operates to analyze samples in containers 180 on the rack 102 that are initially introduced into the sample analyzer 100. The analysis unit 108 includes subsystems for transferring vessels, dispensing reagents, mixing, washing, delivering substrates, and reading the light intensity of chemiluminescent reactions.

[0051] The sample container identification unit 110 is used to identify the type of container 180 in the rack 102. Examples of the sample container identification unit 110 are shown and described herein.

[0052] See also Figures 2 to 4 , illustrates an exemplary operation of the rack 102, which holds and transfers one or more sample containers 180 in the sample analyzer 100. Specifically, Figure 2 A perspective view of an exemplary bracket 102 is shown installed in a first position in an SPU of a sample analyzer 100 . Figure 3 showing a perspective view of the bracket at a second position in the SPU, and Figure 4 A perspective view of the support is shown in a third position, wherein the support is partially moved out of the second position of the SPU. As described below, the support 102 is located Figure 2 In the loading channel 124, located Figure 3 The intersection of the loading channel 124 and the presentation channel 128 in the Figure 4 In the rendering channel 128.

[0053] In some embodiments, the rack 102 is loaded with one or more sample containers 180 before the rack 102 is loaded into the sample analyzer 100 (e.g., the SPU 104 of the sample analyzer). In other embodiments, the rack 102 is loaded with one or more sample containers 180 after the rack 102 has been loaded into the sample analyzer 100 (e.g., the SPU 104 of the sample analyzer). In other embodiments, the rack 102 is partially loaded with one or more sample containers 180 before the rack 102 is loaded into the sample analyzer 100, and one or more additional sample containers 180 may be loaded into the rack 102 subsequently.

[0054] The SPU 104 operates to transfer the rack 102, thereby transferring the sample container 180 held in the rack 102. In some embodiments, the SPU 104 is configured to transfer the rack 102 to various locations or stations in the sample analyzer 100. Figure 2 As shown, the SPU 104 includes a lateral motion section 120 (i.e., a loading-unloading channel) and a transverse motion section 122 (i.e., a presentation channel). As depicted, the lateral motion section 120 is substantially perpendicular to the transverse motion section 122. The lateral motion section 120 includes a loading channel 124 and an unloading channel 126. The presentation channel 128 of the transverse motion section 122 is positioned between the loading channel 124 and the unloading channel 126.

[0055] In some embodiments, the lateral motion section 120 includes a pusher 130 to advance the rack 102 along the loading channel 124 and the unloading channel 126. The lateral motion section 122 includes a carrier 132 to advance the rack 102 along the presentation channel 128. The loading channel 124 includes a first track 136 (i.e., a post-loading track) and a second track 138 (i.e., a pre-loading track). The presentation channel 128 includes a third track 140 (i.e., a post-carrier track, a first hook holder, etc.) and a fourth track 142 (i.e., a front carrier track, a second hook holder, etc.). The unloading channel 126 includes a fifth track 144 (i.e., a post-unloading track) and a sixth track 146 (i.e., a pre-unloading track). The first track 136 and the fifth track 144 are aligned with each other. Similarly, the second track 138 and the sixth track 146 are aligned with each other and are substantially parallel to the first track 136 and the fifth track 144. When the carrier 132 is in the receiving position (e.g., see Figure 2 ), the third track 140 is aligned with the first track 136 and the fifth track 144, and the fourth track 142 is aligned with the second track 138 and the sixth track 146.

[0056] The stent 102 may include mounting features configured to load the stent 102 into the SPU 104. In some embodiments, the mounting features include a first hook 160 disposed at a first end 164 and a second hook 162 disposed at a second end 166 opposite the first end 164. To load the stent 102 into the SPU 104, the first hook 160 engages with the rails 136, 140, and / or 144, and the second hook 162 engages with the rails 138, 142, and / or 146. To facilitate placement of the stent 102 into the SPU 104, a handle 168 (see, e.g., Figure 2 ), and the handle can be manually grasped by an operator. In some embodiments, the stent 102 can be loaded into the SPU 104 via an automated device (e.g., by a robot, a pick and place device, etc.).

[0057] When multiple racks 102 are held by an SPU 104, the racks 102 are typically loaded into the SPU 104 at the loading channel 124. Thus, the racks 102 may be stacked within the SPU 104. For example, the front portion 150 of one of the racks 102 may abut the rear portion 152 of another of the racks 102. When more than two of the racks 102 are held by the SPU 104, the front portion 150 of one of the racks 102 may abut the rear portion 152 of another of the racks 102 positioned in front of it, and the rear portion 152 of one of the racks 102 may abut the front portion 150 of another of the racks 102 positioned behind it. Thus, the pattern of abutting racks 102 may form a stack. The rear portion 152 of the rearmost rack 102 may abut the pusher 130.

[0058] One or more of the racks 102 may be loaded into the SPU 104 at a time. For example, the first hook 160 may engage the track 136 and the second hook 162 may engage the track 138 to load the rack 102 into the loading channel 124. If necessary, (e.g., when other racks 102 are already positioned within the SPU 104), the pusher 130 may be retracted (e.g., moved away from the already positioned racks 102) to make room for the newly added rack(s) 102. When loading one or more of the racks 102 into the SPU 104, the pusher 130 may be advanced (e.g., moved toward the rack 102) to eliminate any excess space between the pusher 130 and the rack(s) 102. One or more of the racks 102 may be loaded into the SPU 104 in front of, between, or behind the rack(s) 102 already positioned within the SPU 104.

[0059] To move the rack(s) 102 (and thus the sample containers loaded thereon) through / into the sample analyzer 100, the pusher 130 can advance the rack(s) 102, thereby positioning at least one of the rack(s) 102 into the presentation channel 128 when the carrier 132 is in the receiving position (e.g., see Figure 2 and Figure 3 When moving from the loading channel 124 to the presentation channel 128, the first hook 160 transfers engagement from the track 136 to the track 140, and the second hook 162 transfers engagement from the track 138 to the track 142. In order to further move the (multiple) racks 102 (and thus further move the sample container) through / into the sample analyzer 100 (e.g., by Figure 4 170), the carrier 132 can be advanced from the receiving position, thereby further advancing at least one of the (multiple) brackets 102 along the presentation channel 128 (see, for example, Figure 3 and Figure 4The sample analyzer 100 may be moved between the sample containers and the sample container 102 to the sample analyzer 100. Upon reaching a predetermined position within the sample analyzer 100, the sample(s) within the one or more sample containers may be extracted and / or otherwise processed and / or analyzed by the sample analyzer 100 and / or within the sample analyzer.

[0060] To remove the rack(s) 102 (and thereby the sample containers loaded thereon) from the sample analyzer 100, the carrier 132 may be retracted from the predetermined position to the receiving position, thereby withdrawing at least one of the rack(s) 102 from the sample analyzer 100 along the presentation channel 128 (see, for example, Figure 4 and Figure 3 In order to reach the receiving position (e.g., by Figure 4 The pusher 130 can then advance the racks 102 so that when the carrier 132 is in the receiving position, at least one of the racks 102 is positioned in the unloading channel 126 (see, for example, Figure 2 and Figure 3 126, but at least one of the racks 102 is pushed out of the carrier 132 and into the unloading channel 126 using the pusher 130 or a stack of racks 102. When moving from the presentation channel 128 to the carrying channel 126, the first hook 160 transfers engagement from the track 140 to the track 144, and the second hook 162 transfers engagement from the track 142 to the track 146. To further move the racks 102 (and thus the sample containers) through / from the sample analyzer 100, additional racks 102 may similarly be ejected from the carrier 132 into the unloading channel 126, thereby pushing at least one of the racks 102 along the unloading channel 126. The racks 102 may similarly be driven away from the end of the unloading channel 126 (e.g., into a waste receptacle) to be unloaded from the sample analyzer 100.

[0061] 14, and / or 146. Alternatively, to unload the racks 102 from the SPU 104, the first hook 160 may be disengaged from the rails 136, 140, and / or 144, and the second hook 162 may be disengaged from the rails 138, 142, and / or 146. To facilitate removal of the racks 102 from the SPU 104, an operator may manually grasp the handle 168. The racks 102 may be unloaded from the SPU 104 via automated means (e.g., by a robot, a pick and place device, etc.). Multiple racks 102 may be held simultaneously by the unloading lane 126 (similar to the loading lane 124). The racks 102 are typically unloaded from the SPU 104 at the unloading lane 126.

[0062] See also Figures 5 to 8, shows an example of a rack 102 loaded with a container 180. Specifically, Figure 5 is a front view of an exemplary pipe rack, and Figure 6 yes Figure 5 Perspective view of the pipe rack. Figure 7 is a front view of an exemplary cup holder, and Figure 8 yes Figure 7 A perspective view of a cup holder.

[0063] The bracket 102 includes a bracket slot 190 that can be loaded with a container 180. FIG. 21A to FIG. 21C As shown in , the bracket slot 190 can define a container position 334.

[0064] In some embodiments, such as Figure 5 and Figure 6 As shown, the rack 102 includes a tube rack 102A. In the illustrated example, the tube rack 102A is loaded with tubes 182 (i.e., examples of the container 180) of different sizes, such as a first tube 182A, a second tube 182B, and a third tube 182C. In this example, one of the rack slots 190 remains empty in the tube rack 102A. As described herein, the sample container identification unit 110 can identify different types of tubes 182.

[0065] In other embodiments, the support 102 includes Figure 7 and Figure 8 102B. In the illustrated example, the cup holder 102B is loaded with cups 184 (i.e., examples of the container 180) of different sizes, such as a first cup 184A, a second cup 184B, and a third cup 184C. In this example, four of the rack slots 190 remain empty in the cup holder 102B. As described herein, the sample container identification unit 110 can identify different types of cups 184.

[0066] Fig. 9 Such as Figure 5 and Figure 6 1 is a perspective cross-sectional view of a rack 102 of a tube rack 102A holding various types of sample tubes 182. As shown, the tube rack 102A is configured to receive sample tubes 182 of different sizes.

[0067] Figures 10 to 13 Various types of sample cups 184 are shown. As shown, the sample cups 184 can be of various types, and the cup holder 102B is configured to accommodate such sample cups 184 of different sizes.

[0068] See also Figures 14 to 18 , an example of a sample container identification unit 110 is described with respect to the support 102. Figures 14 to 18In the embodiment of the present invention, the sample container identification unit 110 is primarily shown relative to the tube holder 102A. However, it should be understood that the sample container identification unit 110 can also be used and operated in a similar manner relative to the cup holder 102B.

[0069] Specifically, Fig.14 is a perspective view of the tube rack 102 positioned in the presentation channel 128 of the SPU 104. Fig.15 Show Fig.14 An enlarged view of the pipe rack 102 is shown. Fig.14 and Fig.15 In FIG. 1 , the tube rack 102 is shown partially according to the camera unit of the sample container identification unit 110 . Fig.16 is another perspective view of a tube rack 102 positioned in a presentation channel 128 of an SPU 104 in part according to a camera unit of a sample container identification unit 110 . Fig.17 is yet another perspective view of a tube rack 102 positioned in a presentation channel 128 of an SPU 104 according in part to a container detection unit of a sample container identification unit 110 .

[0070] The sample container identification unit 110 operates to identify the container 180 in the rack 102 and detects various characteristics associated with the container 180 for determining the type of the container 180. For example, the sample container identification unit 110 operates to detect a container barcode 186 disposed on the container 180. As described herein, the container barcode 186 is used to authenticate the container 180 in the rack 102. The container barcode 186 can be disposed at any suitable location on the container 180. Figure 5 , Figure 6 and Fig.15 In the illustrated example, the container barcode 186 is disposed on the exterior of the sample tube 182. The container barcode 186 may be similarly disposed on the exterior of the sample cup 184.

[0071] In addition, the sample container identification unit 110 operates to identify the rack 102. For example, the sample container identification unit 110 operates to detect a rack barcode 188 disposed on the rack 102. As described herein, the rack barcode 188 is used to authenticate the rack 102. The rack barcode 188 can be disposed at any suitable location on the rack 102. Figure 5 , Figure 6 and Fig.15 In the illustrated example, the holder barcode 188 is disposed on the front of the holder 102 adjacent the first end 164 of the holder 102. Other locations in the holder 102 are possible for the holder barcode 188. The holder barcode 188 may be disposed on the tube holder 102A and / or the cup holder 102B.

[0072] In some embodiments, the sample container identification unit 110 includes a camera unit 202, a container detection unit 204, a screen 206, and a computing device 208. The camera unit 202 may be secured to the SPU 104 using a mounting bracket 210.

[0073] The camera unit 202 operates to detect and identify the rack 102 and the container 180 in the rack 102, and to determine characteristics of the rack 102 and the container 180 therein. As described herein, such characteristics of the container 180 may be used to identify the type of container 180. The camera unit 202 is disposed in front of the rack 102 that is movable relative to the camera unit 202.

[0074] As described herein, the camera unit 202 can operate to read bar codes associated with the rack 102 and the container 180 therein. In addition, the camera unit 202 operates to locate, analyze, and inspect the rack 102 and the container 180 therein. The camera unit 202 can be connected to the computing device 208 for various processes. An example of the camera unit 202 includes the ADVANTACE 100 series, which can be purchased from Cognex Corporation (Natick, MA) in Natick, Massachusetts.

[0075] The camera unit 202 may be supported in the sample analyzer 100 using a mounting bracket 210. The mounting bracket 210 is configured to space the camera unit 202 from the support 102 and position the camera unit 202 relative to the instantaneous position(s) of the support 102 so that the camera unit 202 has a field of view (FOV) on the inspected container 180 and / or the support 102. Fig.18 An example of a mounting bracket 210 is further described and shown.

[0076] The camera unit 202 may include a light source 203, such as an LED light, which is operable to emit light toward the bracket 102 (and toward the screen 206). The screen 206 is used to project light back in the direction of the field of view (FOV) of the camera unit 202 by reflecting the light toward the aperture of the camera. One example of the camera unit 202 includes a model number named ADVANTAGE 102, such as part number ADV102-CQBCKFS1-B, which is available from Cognex Corporation (Natick, MA).

[0077] The container detection unit 204 operates to detect whether the container 180 is present in the rack 102. The container detection unit 204 is arranged to scan the rack 102 when the rack 102 moves relative to the container detection unit 204. In the illustrated example, the container detection unit 204 is arranged on one side of the rack 102, and the other side of the rack 102 faces the camera unit 202. As described herein, the container detection unit 204 can partially or completely detect the rack 102 and determine any container position of the rack 102 (e.g., FIG. 21A to FIG. 21C The container position 334) is shown as being empty or not empty.

[0078] Various sensors can be used for the container detection unit 204. In some examples, the container detection unit 204 includes various types of optical sensors. For example, the container detection unit 204 includes a reflector-type optical sensor (also known as a reflective light chopper or optical reflector) that positions a light emitting element and a light receiving element on the same surface (so that they face the same direction) and is configured to detect the presence of an object and the position of the object based on reflected light from a target object. An example of such a reflector-type optical sensor is the GP2A25J0000F series, which can be purchased from Sharp Corporation (Osaka, Japan) in Osaka, Japan. Other types of optical sensors can also be used for the container detection unit 204, such as an optical chopper (also known as a transmission-type optical sensor), which consists of a light emitting element and a light receiving element that are aligned facing each other in a single package, and functions by detecting light blocking when a target object comes between the two elements.

[0079] The screen 206 is arranged and used with the camera unit 202 to enhance image capture by the camera unit 202. The screen 206 is arranged opposite the camera unit 202 such that the bracket 102 is positioned between the camera unit 202 and the screen 206. The screen 206 is used to project light back in the direction of the field of view (FOV) of the camera unit by reflecting the light towards the aperture of the camera.

[0080] The screen 206 is made of one or more different materials that can provide different reflective strengths. In addition, the screen 206 includes a material configured to increase the scanning range of the bar code. For example, the screen 206 includes a retroreflective sheeting, an example of which includes 3M Co., Ltd. (Maplewood, MN). TM Scotchlite TM Sheet 7610.

[0081] The computing device 208 is connected to the camera unit 202 and operates to process data transmitted from the camera unit 202, such as image processing and evaluation. In addition, the computing device 208 is connected to the container detection unit 204 and operates to detect whether a container is present in the rack. The computing device 208 may include a computer system such as the one described in reference 201. Fig.34 At least some of the components included in the exemplary computing devices shown and described.

[0082] In some embodiments, the computing device 208 runs a software application that processes and evaluates images from the camera unit 202 and determines various characteristics associated with the rack 102 and / or the container 180 in the rack 102. One example of such a software application is Cognex In-Sight Vision software available from Cognex Corporation (Natick, MA), which provides various tools such as edge detection (“Edge”), pattern matching (“Pattern Match”), histogram analysis (“Histogram”), and barcode detection (“ReadIDMax”).

[0083] See also Fig.18 , the mounting bracket 210 is configured to arrange the camera unit 202 in front of the bracket 102 and facing the front portion 150 of the bracket 102. The camera unit 202 is spaced apart from the front portion 150 of the bracket 102 by a distance L1, which may be in the range of about 100 mm to about 200 mm, and the bracket 102 has a height H1, which may be in the range of about 50 mm to about 100 mm. The height H1 of the bracket 102 may be defined as the distance between the bottom 156 and the top 158 of the bracket 102 (see also Figure 5 ). In some embodiments, the mounting bracket 210 is configured to support the camera unit 202 at an angle A relative to the bottom 156 of the bracket 102 so that the field of view (FOV) covers the entire height of the container 180 received in the bracket 102. In some embodiments, the angle A can be in the range of about 90 degrees to about 120 degrees. In other embodiments, other ranges of distance L1, height H1, and angle A are possible.

[0084] Fig.19 is a flow chart of an exemplary method 300 for performing sample container identification with respect to a rack 102. In some embodiments, the method 300 may be performed at least in part by a sample container identification unit 110 having associated devices in a sample analyzer 100. See also Figure 20 to Figure 2 3 to describe method 300.

[0085] The method 300 may begin at operation 302 where the support 102 is operated to move relative to the sample container identification unit 110 toward a first image position 330A.

[0086] The rack 102 can be moved relative to the sample container identification unit 110 to a plurality of predetermined image positions 330 so that different portions of the rack 102 are observed and captured by the sample container identification unit 110. For example, the camera unit 202 of the sample container identification unit 110 may have a field of view (FOV) that is limited to a portion of the rack 102. Therefore, in order to inspect the entire rack 102 (i.e., all rack slots 190 of the rack 102), the rack 102 is moved relative to the camera unit 220 so that the camera unit 220 obtains a plurality of images at a plurality of positions (i.e., image positions 330). Each of the images shows a portion of the rack 102 at a particular position (i.e., a particular image position) of the rack 102. Each portion of the rack 102 (i.e., the rack portion 332) may include one or more container positions 334 in which one or more containers 180 are received, respectively. As described herein, the container positions 334 of the rack 102 correspond to the rack slots 190 of the rack 102.

[0087] like Fig. 20 As shown, in some embodiments, the support 102 has three image positions 330 (such as a first image position 330A, a second image position 330B, and a third image position 330C). In each of the image positions 330, the camera unit 202 is configured to have a field of view (FOV) that captures a portion (i.e., support portion) 332 of the support 102. In the illustrated example, the camera unit 202 can capture an image of the first support portion 332A when the support 102 is in the first image position 330A, capture an image of the second support portion 332B when the support 102 is in the second image position 330B, and capture an image of the third support portion 332C when the support 102 is in the third image position 330C. The image of each support portion 332 can show one or more container positions 334.

[0088] exist FIG. 21A to FIG. 21CIn the illustrated example, the first image 350 is captured when the rack 102 is in the first image position 330A. The first image 350 shows a first rack portion 332A of the rack 102, which includes a first container position 334A and a second container position 334B in the rack 102. The second image 352 is captured when the rack 102 is in the second image position 330B. The second image 352 shows a second rack portion 332B of the rack 102, which includes a third container position 334C and a fourth container position 334D in the rack 102. The third image 354 is captured when the rack 102 is in the third image position 330C. The third image 354 shows a third rack portion 332C of the rack 102, which includes a fifth container position 334E, a sixth container position 334F, and a seventh container position 334G in the rack 102.

[0089] In some embodiments, the images 350 , 352 , and 354 captured by the camera unit 202 of the sample container identification unit 110 may be low exposure monochrome images. FIG. 21A to FIG. 21C The images 350 , 352 , and 354 shown illustrate the tube rack 102A with the sample tube 182 . Fig. 22 An image 356 of a portion of the cup holder 102B with the sample cup 184 is shown.

[0090] At operation 304, when the rack 102 moves toward the first image position 330A, the presence or absence of one or more containers 180 in the rack portion 332A of the rack 102 is detected. As described herein, the container detection unit 204 can be operated to perform container presence detection. The rack portion 332A is a portion of the rack 102 that is included in the field of view (FOV) of the camera unit 202 of the sample container identification unit 110 at or near the first image position 330A. In some embodiments, when the rack 102 moves toward the first image position 330A, the container detection unit 204 can be operated to detect the presence of containers in the rack portion (e.g., the first rack portion 332A) of the rack 102. In other embodiments, when the rack 102 is located near the first image position 330A or at the first image position 330A, the container presence can be detected.

[0091] At operation 306, it is determined whether there are any containers 180 in the rack portion 332A of the rack 102. If there are any containers 180 ("yes" in this operation), the method 300 proceeds to operation 308. If no container 180 is detected ("no" in this operation), the method 300 moves to operation 316, in which the rack 102 moves to the next image position 330 (e.g., 330B after 330A). Therefore, if no container is found at a particular image position 330, the rack 102 can skip the particular image position. For example, the rack 102 can jump to the next image position 330 without performing container identification operations (such as operations 308 and 310) at the particular image position, thereby saving time and resources.

[0092] At operation 308, the sample container identification unit 110 operates to detect one or more container barcodes 186 associated with the container 180. The sample container identification unit 110 may also operate to authenticate the container 180 based on the detected container barcode 186. In some embodiments, the support 102 stops at the image position 330 for barcode detection. For example, Fig.23A As shown, the sample container identification unit 110 (e.g., its camera unit 202) operates to capture an image 340 of a portion of the rack 102 having the sample tube 182. In some embodiments, the image 340 is a high exposure monochrome image for barcode detection. Once the image 340 is captured, the sample container identification unit 110 operates to identify the container barcode 186 in the image 340 and read the container barcode 186 to authenticate the container 180 (i.e., the sample tube 182 in this example). Fig. 23B As shown in the rectangular box 344 in the image 340, the container bar code 186 is identified in the image 340. Various image processing methods can be used to identify and read the container bar code. An example of such an image processing method is Cognex In-Sight Vision software available from Cognex Corporation (Natick, MA), which provides various tools such as edge detection ("Edge"), pattern matching ("Pattern Match"), histogram analysis ("Histogram"), and bar code detection ("ReadIDMax").

[0093] In addition, the sample container identification unit 110 can be operated to detect a rack barcode 188 disposed on the rack 102 and authenticate the rack 102 based on the rack barcode 188. The rack barcode 188 is detected and read in a similar manner to the container barcode 186 described above. For example, Fig.23AAs shown, the image 340 captured by the sample container identification unit 110 (e.g., the camera unit 202 thereof) may include a portion of the rack 102 having the rack barcode 188. Once the image 340 is captured, the sample container identification unit 110 operates to identify the rack barcode 188 in the image 340 and read the rack barcode 188 to authenticate the container 180. Fig. 23B As shown in the rectangular box 346 in the image 340, the stent barcode 188 is identified in the image 340. Various image processing methods can be used to identify and read the stent barcode. An example of such an image processing method is Cognex In-Sight Vision software available from Cognex Corporation (Natick, MA), which provides various tools such as edge detection ("Edge"), pattern matching ("Pattern Match"), histogram analysis ("Histogram"), and barcode detection ("ReadIDMax").

[0094] At operation 310, the sample container identification unit 110 operates to determine the characteristics of the container 180. In some embodiments, the rack 102 remains stationary for determining the characteristics of the container. As described herein, the sample container identification unit 110 operates to process an image of the rack 102 with the container 180 (such as FIG. 21A to FIG. 21C as well as Fig. 22 350, 352, 354 and 356 in the image 350, 352, 354 and 356) and determine various characteristics associated with the container 180, such as the size of each container (e.g., height and width) and the presence of a lid on the container. As described in more detail below, such characteristics can be used to identify the type of container. Various image processing methods can be used to determine such characteristics of the containers in the support. An example of such image processing methods is Cognex In-Sight Vision software available from Corning Corporation (Natick, MA) in Natick, Massachusetts, which provides various tools such as edge detection ("Edge"), pattern matching ("Pattern Match"), histogram analysis ("Histogram"), and barcode detection ("ReadIDMax").

[0095] At operation 312, it is determined whether the entire rack 102 has been inspected. In some embodiments, it is determined whether the rack 102 has moved through all predetermined image positions 330. In other embodiments, it is determined whether all rack portions 332 of the rack 102 have been captured by the camera unit 202. In other embodiments, it is determined whether all container positions 334 of the rack 102 have been captured by the camera unit 202.

[0096] If it is determined that the entire rack 102 has been inspected ("yes" in this operation), the method 300 moves to operation 314, in which the rack 102 is moved to another location inside or outside the sample analyzer 100 for subsequent processing. Otherwise ("no" in this operation), the method 300 moves to operation 316, in which the rack 102 is moved to the next image position 330 (e.g., 330B after 330A). When the rack 102 moves to the next image position 330 or when the rack 102 is at or adjacent to the next image position 330, operation 304 and subsequent operations are performed as described above. In some embodiments, when performing operation 304 and subsequent operations, if the rack barcode has been read once (such as shown in operation 308), the rack barcode reading can be omitted.

[0097] Fig.24 is a flow chart of another exemplary method 400 for performing sample container identification with respect to the rack 102. In some embodiments, the method 400 may be performed at least in part by the SPU 104, the sample container identification unit 110, and / or other devices in the sample analyzer 100.

[0098] The method 400 may begin at operation 402, in which the rack 102 is moved to enter the presentation channel 128. In some embodiments, the carrier 132 operates to advance the rack 102 into the presentation channel 128, such as from Figure 3 Move to the position shown Figure 4 Position shown.

[0099] As shown, the rack 102 is oriented to move along the presentation channel 128 toward the sample container identification unit 110 so that the first rack portion 332A of the rack 102 (including the first container position 334A and the second container position 334B in this example) first approaches the sample container identification unit 110.

[0100] At operation 404, the sample container identification unit 110 operates the container detection unit 204 to detect the presence of any container 180 in the first rack portion 332A of the rack 102. Fig.19 404 is performed according to operation 304 in the embodiment. In the illustrated example, the first rack portion 332A of the rack 102 includes a first container position 334A and a second container position 334B, and therefore, the container detection unit 204 operates to detect whether either of the first container position 334A and the second container position 334B is occupied by the container 180, or whether both of the first container position 334A and the second container position 334B are occupied by the container 180.

[0101] Thus, when the rack 102 is introduced into the presentation channel 128 and moves toward the first image position 330A, the container detection unit 204 performs a first close-up check for the presence of a container in the first rack portion 332A of the rack 102, such as Fig.17 shown.

[0102] The container detection unit 204 may include one or more sensors of various types. In some examples, the container detection unit 204 includes various types of optical sensors. For example, the container detection unit 204 includes a reflector-type optical sensor (also known as a reflected light chopper or optical reflector) that positions a light emitting element and a light receiving element on the same surface (so that they face the same direction) and is configured to detect the presence and location of an object based on reflected light from a target object. An example of such a reflector-type optical sensor is the GP2A25J0000F series, which can be purchased from Sharp Corporation (Osaka, Japan) in Osaka, Japan. Other types of optical sensors may also be used for the container detection unit 204.

[0103] At operation 406, if any container 180 is detected in the first rack portion 332A of the rack 102, the sample container identification unit 110 operates to store information indicating that at least one container is included in the rack. For example, if it is determined at operation 404 that the rack 102 (e.g., its first rack portion 332A) includes one or two containers 180, the sample container identification unit 110 operates to set the container presence flag ("at least one container presence flag") to true.

[0104] At operation 408 , the support 102 continues to move to the first image location 330A and stops at the first image location 330A. For example, the carrier 132 operates to continuously move the support 102 to the first image location 330A and stops the support 102 there.

[0105] As described in this article, Fig.21A and Fig.23A As shown, the first image position 330A may be a position of the rack 102 relative to the camera unit 202, wherein one or more containers 180 secured at a first container portion 332A including a first container position 334A and a second container position 334B may be at least partially captured by the camera unit 202. In the illustrated example, the rack barcode 188 disposed on the rack 102 is also observed at the first image position 330A.

[0106] At operation 410, the sample container identification unit 110 operates the camera unit 202 to read the container barcode 186 of each container 180 received in the first rack portion 332A of the rack 102 (which includes the first container position 334A and / or the second container position 334B). Fig.19 In some embodiments, the camera unit 202 is operated to capture an image of the first support portion 332A of the support 102 (such as Fig.21A 350 in the image), and processes the image to detect and read the container barcode 186 (eg, the container barcode 186 of the container 180 at the first container position 334A and the second container position 334B) Fig.23A and Fig. 23B shown).

[0107] Once the container barcode 186 is read, the sample container identification unit 110 may identify the container 180 based on the detected container barcode 186. The sample container identification unit 110 may store identification information of the container 180 (eg, container ID(s)).

[0108] In some embodiments, the sample container identification unit 110 operates to compare the detected container barcode 186 with information provided by the user (e.g., information about the container input by the user, which can be received through an input device of the sample analyzer 100), and determine whether the container barcode 186 matches the user input. The sample container identification unit 110 can be operated to store information indicating that a particular container position 334 (e.g., 334A and / or 334B) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 can be operated to mark the container position 334 (e.g., the first container position 334A and / or the second container position 334B) in the rack 102 that holds a container with a non-matching container barcode 186.

[0109] In addition, the sample container identification unit 110 also operates the camera unit 202 to read the rack barcode 188 of the rack 102. In the illustrated example, the rack barcode 188 is disposed adjacent to the first rack portion 332A of the rack 102 (close to the first end 164 of the rack 102). Therefore, the image of the first rack portion 332A of the rack 102 (such as Fig.21A The first image 350 in FIG. 1 includes the rack barcode 188 of the rack 102 . The sample container identification unit 110 processes the image to detect and read the rack barcode 188 of the rack 102 .

[0110] Once the rack barcode 188 is read, the sample container identification unit 110 may identify the rack 102 based on the detected rack barcode 188. The sample container identification unit 110 may store identification information of the rack 102 (eg, a rack ID).

[0111] Various image processing methods may be used to identify and read the barcodes 186 and 188. One example of such an image processing method is the Cognex In-Sight Vision software available from Cognex Corporation (Natick, MA), which provides various tools such as edge detection (“Edge”), pattern matching (“PatternMatch”), histogram analysis (“Histogram”), and barcode detection (“ReadIDMax”).

[0112] At operation 412, the sample container identification unit 110 may be operated to determine whether the detected rack barcode 188 is valid. If it is determined that the rack barcode 188 is valid ("yes" at this operation), the method 400 proceeds to operation 414. Otherwise ("no" at this operation), the method 400 jumps to operation 448, in which the rack 102 is moved to the unloading channel 126. At operation 448, the sample analyzer 100 may be operated to alert the user of the invalidity of the rack as determined at operation 412. The alert can be of various types, such as a visual and / or audible alert or notification by the sample analyzer 100.

[0113] At operation 414, the sample container identification unit 110 may operate the camera unit 202 to determine the characteristics of the (multiple) containers 180 at the first rack portion 332A of the rack 102. Fig.19 Operation 414 is performed based on operation 310 in the example.

[0114] For example, the sample container identification unit 110 operates to process an image of the first rack portion 332A of the rack 102 (such as Fig.21A 350 in the first image 350) and determine various characteristics associated with the containers 180, such as the size of each container (e.g., height and width) and the presence of a lid on the container. As described in more detail below, such characteristics can be used to identify the type of container. Fig.25 An exemplary detailed method of performing operation 414 is described and illustrated.

[0115] In addition, the sample container identification unit 110 can operate the camera unit 202 to determine the characteristics of the rack 102, similar to determining the characteristics of the container. In some embodiments, an image of the first rack portion 332A of the rack 102 (such as Fig.21AIn other embodiments, the stent barcode 188 identified from the captured image can be used to determine the stent characteristics.

[0116] In some embodiments, the data of the container characteristics and / or rack characteristics obtained above may be stored in the sample container identification unit 110. In some embodiments, if the container(s) have predetermined undesirable characteristics (e.g., uncapped, unapproved, and / or inappropriate container positions), the sample container identification unit 110 may store information indicating that a particular container position 334 (e.g., 334A and / or 334B) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 may be operable to mark the container positions 334 (e.g., first container position 334A and / or second container position 334B) in the rack 102 that hold containers having such undesirable characteristics.

[0117] At operation 416, the support 102 is operated to move toward the second image location 330B. Fig.21B As shown, the second image position 330B can be a position of the bracket 102 relative to the camera unit 202, wherein (multiple) containers 180 fixed at the second container portion 332B including the third container position 334C and the fourth container position 334D can be at least partially captured by the camera unit 202.

[0118] At operation 418, the sample container identification unit 110 operates the container detection unit 204 to detect the presence of any container 180 in the second rack portion 332B of the rack 102. Fig.19 404 or the above operation 404 to perform operation 418. In the illustrated example, the second rack portion 332B of the rack 102 includes a third container position 334C and a fourth container position 334D, and therefore, the container detection unit 204 operates to detect whether any one of the third container position 334C and the fourth container position 334D is occupied by the container 180, or whether both of the third container position 334C and the fourth container position 334D are occupied by the container 180.

[0119] As such, the container detection unit 204 performs a second close-fly check for the presence of a container in the second rack portion 332B of the rack 102 as the rack 102 moves toward the second image location 330B.

[0120] At operation 420, if any container 180 is detected in the second rack portion 332B of the rack 102, the sample container identification unit 110 operates to store information indicating that at least one container is included in the rack. For example, if it is determined at operation 418 that the rack 102 (e.g., its second rack portion 332B) includes one or two containers 180, the sample container identification unit 110 operates to set the container presence flag ("at least one container presence flag") to true.

[0121] At operation 422, it is determined whether there are any containers at the second rack portion 332B (e.g., either or both of the third container position 334C and the fourth container position 334D) of the rack 102. If it is determined that there are any containers at the second rack portion 332B ("Yes"), the method 400 continues to operation 424. Otherwise ("No"), the method 400 jumps to operation 448.

[0122] At operation 424, the support 102 is stopped and brought to rest at the second image location 330B.

[0123] At operation 426, the sample container identification unit 110 operates the camera unit 202 to read the container barcode 186 of each container 180 received in the second rack portion 332B of the rack 102 (which includes the third container position 334A and / or the fourth container position 334D). Operation 418 is similar to Fig.19 In some embodiments, the camera unit 202 operates to capture an image of the second support portion 332B of the support 102 (such as Fig.21B 354B), and processes the image to detect and read the container bar codes 186 of the containers 180 at the third container position 334C and the fourth container position 334D.

[0124] Once the container barcode 186 is read, the sample container identification unit 110 may identify the container 180 based on the detected container barcode 186. The sample container identification unit 110 may store identification information of the container 180 (eg, container ID(s)).

[0125] In some embodiments, the sample container identification unit 110 operates to compare the detected container barcode 186 with information provided by the user (e.g., information about the container input by the user, which can be received through an input device of the sample analyzer 100), and determines whether the container barcode 186 matches the user input. The sample container identification unit 110 can be operated to store information indicating that a particular container position 334 (e.g., 334C and / or 334D) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 can be operated to mark the container position 334 (e.g., the first container position 334C and / or the second container position 334D) in the rack 102 that holds a container with a non-matching container barcode 186.

[0126] In some embodiments, the sample container identification unit 110 also operates to cross-check whether the container 180 identified at the second image location 330B matches (or is compatible with) the identification of the rack 102 (eg, the rack ID found at operation 410 ).

[0127] At operation 428, the sample container identification unit 110 may operate the camera unit 202 to determine the characteristics of the (multiple) containers 180 at the second rack portion 332B of the rack 102. Fig.19 Operation 414 may be performed by operation 310 in or operation 414 above.

[0128] For example, the sample container identification unit 110 operates to process an image of the second rack portion 332B of the rack 102 (such as Fig.21B 352 in the second image 352) and determine various characteristics associated with the containers 180, such as the size of each container (e.g., height and width) and the presence of a lid on the container. As described in more detail below, such characteristics can be used to identify the type of container. Fig.25 An exemplary detailed method of performing operation 428 is described and illustrated.

[0129] In some embodiments, the data of the container characteristics obtained above may be stored in the sample container identification unit 110. In some embodiments, if the container(s) have predetermined undesirable characteristics (e.g., uncapped, unapproved, and / or inappropriate container positions), the sample container identification unit 110 may store information indicating that a particular container position 334 (e.g., 334C and / or 334D) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 may be operative to mark the container positions 334 (e.g., third container position 334C and / or fourth container position 334D) in the rack 102 that hold containers having such undesirable characteristics.

[0130] At operation 430, the support 102 is operated to move toward the third image position 330C. As described herein, Fig. 21C As shown, the third image position 330C can be a position of the bracket 102 relative to the camera unit 202, wherein (multiple) containers 180 fixed at the third container portion 332C including the fifth container position 334E, the sixth container position 334F and the seventh container position 334G can be at least partially captured by the camera unit 202.

[0131] At operation 432, the sample container identification unit 110 operates the container detection unit 204 to detect the presence of any container 180 in the third rack portion 332C of the rack 102. Fig.19 Operation 432 is performed by operation 304 in or operation 404 or 418 above. In the illustrated example, the third rack portion 332C of the rack 102 includes a fifth container position 334E, a sixth container position 334F, and a seventh container position 334G, and therefore, the container detection unit 204 operates to detect whether any or all of the fifth container position 334E, the sixth container position 334F, and the seventh container position 334G are occupied by one container 180 or multiple containers 180.

[0132] As such, when the rack 102 moves toward the third image location 330C, the container detection unit 204 performs a third close fly-by check for the presence of a container in the third rack portion 332C of the rack 102 .

[0133] At operation 434, if any container 180 is detected in the third rack portion 332C of the rack 102, the sample container identification unit 110 operates to store information indicating that at least one container is included in the rack. For example, if it is determined at operation 432 that the rack 102 (e.g., its third rack portion 332B) includes one or two containers 180, the sample container identification unit 110 operates to set the container presence flag ("at least one container presence flag") to true.

[0134] At operation 436, the sample container identification unit 110 operates to determine the state (true or false) of the container presence flag ("at least one container presence flag"). If the state is true ("true"), the method 400 proceeds to operation 438. Otherwise ("false"), the method 400 jumps to operation 448.

[0135] At operation 438, it is determined whether there are any containers at the third rack portion 332C (e.g., any or all of the fifth container position 334E, the sixth container position 334F, and the seventh container position 334G) of the rack 102. If it is determined that there are any containers at the third rack portion 332C ("Yes"), the method 400 continues to operation 440. Otherwise ("No"), the method 400 jumps to operation 446.

[0136] At operation 440 , the support 102 is stopped and brought to rest at the third image position 330C.

[0137] At operation 442, the sample container identification unit 110 operates the camera unit 202 to read the container barcode 186 of each container 180 received in the third rack portion 332C of the rack 102 (which includes the fifth container position 334E, the sixth container position 334F, and the seventh container position 334G). Operation 418 is similar to Fig.19 In some embodiments, the camera unit 202 operates to capture an image of the third support portion 332C of the support 102 (such as Fig. 21C 354 in the image), and processes the image to detect and read the container bar codes 186 of the containers 180 at the fifth container position 334E, the sixth container position 334F, and the seventh container position 334G.

[0138] Once the container barcode 186 is read, the sample container identification unit 110 may identify the container 180 based on the detected container barcode 186. The sample container identification unit 110 may store identification information of the container 180 (eg, container ID(s)).

[0139] In some embodiments, the sample container identification unit 110 operates to compare the detected container barcode 186 with information provided by the user (e.g., information about the container input by the user, which can be received through an input device of the sample analyzer 100), and determines whether the container barcode 186 matches the user input. The sample container identification unit 110 can be operated to store information indicating that a particular container position 334 (e.g., 334E, 334F, and / or 334G) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 can be operated to mark the container position 334 (e.g., the fifth container position 334E, the sixth container position 334F, and / or the seventh container position 334G) in the rack 102 that holds a container with a non-matching container barcode 186.

[0140] In some embodiments, the sample container identification unit 110 also operates to cross-check whether the container 180 identified at the third image location 330C matches (or is compatible with) the identification of the rack 102 (e.g., the rack ID found at operation 410).

[0141] At operation 444, the sample container identification unit 110 may operate the camera unit 202 to determine the characteristics of the (multiple) containers 180 at the third rack portion 332C of the rack 102. Fig.19 Operation 414 may be performed by operation 310 in or operation 414 or 428 above.

[0142] For example, the sample container identification unit 110 operates to process an image of the third rack portion 332C of the rack 102 (such as Fig. 21C 354 in the image 354) and determine various characteristics associated with the containers 180, such as the size of each container (e.g., height and width) and the presence of a lid on the container. As described in more detail below, such characteristics can be used to identify the type of container. Fig.25 An exemplary detailed method of performing operation 444 is described and illustrated.

[0143] In some embodiments, the data of the container characteristics obtained above may be stored in the sample container identification unit 110. In some embodiments, if the container(s) have predetermined undesirable characteristics (e.g., uncapped, unapproved, and / or inappropriate container positions), the sample container identification unit 110 may store information indicating that a particular container position 334 (e.g., 334E, 334F, and / or 334G) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 may be operative to mark the container positions 334 (e.g., the fifth container position 334E, the sixth container position 334F, and / or the seventh container position 334G) in the rack 102 that hold containers having such undesirable characteristics.

[0144] At operation 446, the rack 102 is moved to an aliquoting and / or pipetting system for sample processing.

[0145] In some embodiments, information output from the SPU to the aliquot and / or pipetting system using the sample container identification unit 110 includes information about a barcode that can be used to prioritize sample aspiration and indicate the type of sample (e.g., low volume, STAT, and calibration samples). Information from the SPU using the sample container identification unit 110 can also include visual information such as the container type that can be determined from a container type library. Information that can be provided to the sample pipette can include starting level sensing to detect the starting position of the liquid (top of the container), the maximum allowed travel depth during aspiration (liquid dead volume or container bottom), and the internal geometry of the sample container (which can be used for accurate aspiration to cause the SPU and any other deflections required by the pipette).

[0146] At operation 448, once sample processing is performed at operation 446, the rack 102 is moved to the unloading channel 126. In addition, the sample analyzer 100 may be operable to alert the user of a number of messages, such as the invalidity of the rack determined at operation 412, the state of the container presence flag determined at operation 436 (i.e., false), or the end of sample processing performed at operation 446. The alert may be of various types, such as a visual and / or audible alert or notification by the sample analyzer 100.

[0147] As described above, if a container is not found at a particular image position 330, the rack 102 may skip over the particular image position. For example, the rack 102 may jump to the next image position 330 without performing a container identification operation at the particular image position. Thus, bypassing the algorithm to bypass the visual inspection may save time. The main instrument has a cycle time (e.g., 8 seconds), and the SPU operation is partially independent of the main instrument, but ideally completes within 8 seconds. For example, if there are racks with incorrect numbers, bypassing allows them to be quickly cleared. Thus, due to the bypass, the main instrument does not need to wait for the SPU to complete its operation.

[0148] Fig.25 is a flow chart of an exemplary method 500 for processing an image of a scaffold having one or more vessels and determining characteristics of the vessels therein. In some embodiments, the method 500 is used to perform Fig.24 In some embodiments, method 500 may be performed at least in part by SPU 104, sample container identification unit 110, and / or other devices in sample analyzer 100. See also Figure 26 to Figure 32 Method 300 is described.

[0149] The method 500 may begin at operation 502, where a stent reference 520 is identified in a captured image. In some embodiments, a first hook 160 (also referred to herein as a front tab) of a stent 102 is used as the stent reference 520. The first hook 160 may be detected in a captured image (e.g., first image 350) when the stent 102 is in a first stop position (e.g., first image position 330A).

[0150] For example, edge 522 ( Figure 5 ) is predetermined as the support reference 520. Fig.26 As shown, a predetermined edge 522 of the rack 102 can be identified by the sample container identification unit 110 in the first image 350. In this illustration, the identified edge 522 of the rack 102 is indicated as a line 524, which is an icon representing the camera unit 202 identifying the edge 522. In this embodiment, the X-axis assumes that the rack 102 is fully engaged.

[0151] At operation 504, the sample container identification unit 110 operates to create one or more regions of interest 528 (also referred to herein as height regions of interest) for container height detection. In some embodiments, three regions of interest 528 (including 528A, 528B, and 528C) are created relative to the support reference 520, such as by offsetting the support reference 520 on the Y axis.

[0152] exist Fig. 27 In the illustrated example, in the image 350, the first region of interest 528A is created and arranged to be centered on the support reference 520 on the Y axis. The second region of interest 528B is created and arranged to be separated from the support reference 520 by a predetermined distance (e.g., Fig. 27 The third region of interest 528C is created and arranged to be offset from the first region of interest 528A by a predetermined distance (e.g., Fig. 27 Alternatively, the third region of interest 528C may be created by offsetting from the first region of interest 528A.

[0153] For each of the regions of interest 528, the sample container identification unit 110 operates to detect the top tube edge 530 (e.g., 530A, 530B, and 530C) and determine the height of the associated container 180. Fig. 27 In the illustrated example, the height of the container 180 associated with the second region of interest 528B is measured to be 1178.34 pixels, and the height of the container 180 associated with the third region of interest 528C is measured to be 1193.10 pixels.

[0154] In some embodiments, a result indicating that a container has not been detected may be generated instead of reporting the height of the container. For example, no container exists in the first region of interest 528A, and therefore, a no container detection result will be output. In other embodiments, the sample container identification unit 110 operates to use the top tube edge 530A in the first region of interest 528A to determine the X coordinate measurement of the rack.

[0155] At operation 506, the sample container identification unit 110 operates to create one or more regions of interest 534 (also referred to herein as width regions of interest) for container width (or diameter) detection. In some embodiments, the width regions of interest 534 are formed at a preset distance above the support 102 (on the X-axis) and are centered on the entire height region of interest 528. The width regions of interest 534 are arranged to be transverse to the height region of interest 528, respectively. In some embodiments, the width (i.e., Y-axis distance) of each width region of interest 534 can be preset, such as Fig.28 250 pixels wide.

[0156] For each of the width regions of interest 534, the sample container identification unit 110 operates to detect two opposing sides 536A and 536B of the container and determine the width of the associated container 180. Fig.28 In the illustrated example, the width of the container 180 associated with the area of ​​interest 534A is measured to be 152.99 pixels (i.e., the pixel distance between the opposing sides 536A and 536B), and the width of the container 180 associated with the area of ​​interest 534B is measured to be 151.74 pixels (i.e., the pixel distance between the opposing sides 536A and 536B).

[0157] At operation 508 , the sample container identification unit 110 operates to create one or more regions of interest 540 (also referred to herein as histogram regions of interest) for histogram analysis.

[0158] In some embodiments, three histogram regions of interest 540 (including 540A, 540B, and 540C) are created relative to the top of each height region, such as by being offset in the X-axis from the top tube edge 530. In some embodiments, the histogram regions of interest 540 are created at a preset distance in the X-axis from the top tube edge 530 (e.g., 5 pixels from the top tube edge 530) while the detection of the container occurs. In some embodiments, the size of each histogram region of interest 540 can be predetermined.

[0159] Once the histogram regions of interest 540 are created, the histogram values ​​for each of the histogram regions of interest 540 are obtained. Fig.29In the illustrated example, the histogram value of the region of interest 540B associated with the second region of interest 528B is measured to be 177.62, and the histogram value of the region of interest 540C associated with the third region of interest 528C is measured to be 42.53.

[0160] In some embodiments, the histogram analysis at operation 508 may also detect the presence of a lid on the container. Fig.30 As shown, measurements of the histogram region of interest 540 may indicate whether a cover is present. In some embodiments, a low histogram value may indicate that a cover is present in the location, and a high histogram value may indicate that a cover is not present at the location. Fig.30 In the example of , the average histogram value of the region of interest 540D above the cover 542 of the container 180 was measured to be 16.08 (a relatively low value), and the average histogram value of the regions of interest 540E and 540F above the container 180 without the cover was measured to be 145.81.

[0161] At operation 510, the sample container identification unit 110 operates to compare the information obtained at the above operation with the classification table 550 ( Fig.31 For example, for each container, the height value, the width value, and / or the histogram value may be compared with the values ​​in the classification table 550, and the type of the container may be determined based on the comparison.

[0162] like Fig.31 As shown, a classification table 550 is provided to classify different types of containers (first column) based on height, width and histogram values. For each type of container, the height, width and histogram value can be set with a minimum value, a maximum value and an average value.

[0163] By way of example, if the height value obtained in method 500 is between H5 and H6, the width value obtained in method 500 is between W5 and W6, and the histogram value obtained in method 500 is between HS5 and HS6, then the container of interest can be identified as a 12x65 or 13x75 mm tube with a cap (second row of table 550).

[0164] like Fig.32 As shown, in the case where the holder 102 is a cup holder 102B having a sample cup 184, the same method 500 can be applied to identify the type of the sample cup 184. As described above, the measurements of the histogram area of ​​interest 540 indicate which types of cups are present. The histogram data can be combined with other measurements such as height and width (diameter) to determine the type of cup in the cup holder 102B.

[0165] Fig.33is a flow chart of an exemplary method 600 for adding and verifying a new container in a classification table 550 (ie, a container library, a list of approved containers, etc.).

[0166] At step 602, the user enters information about the new sample container. This information may include the type of container, internal geometry, volume, manufacturer part number, external dimensions, etc. At step 604, the user loads the rack with the container of interest to be added to the classification table 550 by the software (i.e., SW). The user also fills the container to the maximum volume and loads the rack 102 into the loading channel 124 of the SPU 104.

[0167] When the user enters new sample container information (at operation 602), the sample analyzer 100 (e.g., the software application herein) operates to prompt the user to provide wash buffer or deionized water to the maximum volume, place the new sample container in the rack 102, and load it onto the SPU 104 (at operation 604). At operation 602, the information may include information about the manufacturer, part number, container type (e.g., tube or cup), plasma or serum gel matrix in the tube) internal container geometry, insert / cup, (i.e., cup seated in the tube) and / or volume capacity.

[0168] Then, at operation 606, the SPU (including the sample container identification unit 110 therein) operates to identify the sample rack and the size of the container therein. In some embodiments, the information obtained includes the height in the rack (e.g., where the pipette should start liquid level sensing and step from the home position), the diameter, and the histogram value at the top of each container.

[0169] At operation 608, it is determined whether the new sample container is a gel or an insert / cup, etc. If operation 608 determines that the container is an insert / cup / etc., then at operation 610, the aliquot pipette moves to detect the bottom of the container. If operation 608 determines that the container is a gel tube, then the aliquot pipette begins aliquoting from near the top of the liquid in the container.

[0170] At steps 608-618, the sample analyzer 100 (i.e., the instrument) processes the new container and observes characteristics of the new container, as measured by various detection functions of the sample analyzer 100. For example, to measure volume at step 616, all fluid is transferred from the container to the sample vessel (i.e., SV), and the sample vessel is transferred to the wheel washer (i.e., WW).

[0171] As described herein, the present disclosure can address the need for users to load mixed types of sample containers into a single rack. Different types of racks (e.g., tube racks and cup racks) can each hold multiple sample container types. Each of the different types of racks interfaces with the analyzer in the same manner, eliminating the hassle of sorting sample containers and eliminating the error of a user loading a sample container into the wrong sample rack that could result in a QNS (quantity not enough) error.

[0172] The present disclosure is also used as general process monitoring on the sample presentation unit (SPU) to identify the container type for accurate aspiration of samples, detect user errors of placing capped sample containers in sample racks, and also detect user errors associated with placing containers in the wrong position in the sample rack other than the assigned position when sequencing tests. There are container libraries (e.g., tube libraries and cup libraries). The characteristics determined by the vision system are matched with the library to determine which vessel is in each occupied rack position.

[0173] Fig.34 1 shows an exemplary architecture of a computing device that can be used to implement various aspects of the present disclosure, including the sample analyzer 100 or various systems of the sample analyzer 100, such as the sample container identification unit 110 and other sub-units or sub-devices. In addition, one or more devices or units included in the system of the sample analyzer 100 can also utilize the following example: Fig.34 At least some of the components of the computing device shown are implemented. In this document, such a computing device is designated as reference numeral 700. The computing device 700 is used to run the operating system, application programs, and software modules (including software engines) described herein.

[0174] In some embodiments, computing device 700 includes at least one processing device 702, such as a central processing unit (CPU). A variety of processing devices are available from a variety of manufacturers, for example, Intel or Advanced Micro Devices. In this example, computing device 700 also includes system memory 704 and a system bus 706 that couples various system components including system memory 704 to processing device 702. System bus 706 is one of many different types of bus structures, including a memory bus or memory controller; a peripheral bus; and a local bus using any of a variety of bus architectures.

[0175] Examples of computing devices suitable for computing device 700 include desktop computers, laptop computers, tablet computers, mobile devices (such as smartphones, mobile digital device or other mobile device) or other device configured to process digital instructions.

[0176] System memory 704 includes read-only memory 708 and random access memory 710. A basic input / output system 712, containing the basic routines used to transfer information within computing device 700, such as during startup, is typically stored in read-only memory 708.

[0177] In some embodiments, the computing device 700 also includes a secondary storage device 714, such as a hard disk drive, for storing digital data. The secondary storage device 714 is connected to the system bus 706 via a secondary storage interface 716. The secondary storage device and its associated computer-readable media provide non-volatile storage of computer-readable instructions (including applications and program modules), data structures, and other data for the computing device 700.

[0178] Although the exemplary environment described herein uses a hard disk drive as a secondary storage device, in other embodiments other types of computer readable storage media are used. Examples of these other types of computer readable storage media include cassette tapes, flash memory cards, digital video disks, Bernoulli boxes, compact disk read-only memories, digital versatile disk read-only memories, random access memories, or read-only memories. Some embodiments include non-transitory media.

[0179] A number of program modules may be stored in secondary storage device 714 or memory 704 , including an operating system 718 , one or more application programs 720 , other program modules 722 , and program data 724 .

[0180] In some embodiments, the computing device 700 includes input devices to enable a user to provide input to the computing device 700. Examples of input devices 726 include a keyboard 728, a pointer input device 730, a microphone 732, and a touch-sensitive display 740. Other embodiments include other input devices 726. The input devices are typically connected to the processing device 702 through an input / output interface 738 coupled to the system bus 706. These input devices 726 can be connected through many different input / output interfaces, such as a parallel port, a serial port, a game port, or a universal serial bus. Wireless communication between the input device and the interface 738 is also possible, and in some possible embodiments, includes infrared, Wireless technology, WiFi technology (802.11a / b / g / n, etc.), cellular and / or other radio frequency communication systems.

[0181] In this exemplary embodiment, a touch-sensitive display device 740 is also connected to the system bus 706 via an interface such as a video adapter 742. The touch-sensitive display device 740 includes a touch sensor for receiving input from a user when the user touches the display. These sensors may be capacitive sensors, pressure sensors, or other touch sensors. The sensor detects not only contact with the display, but also the location of the contact and the movement of the contact over time. For example, a user may move a finger or stylus on the screen to provide written input. The written input is evaluated and, in some embodiments, converted into text input.

[0182] In addition to the display device 740 , the computing device 700 may also include various other peripheral devices (not shown) such as a speaker or a printer.

[0183] The computing device 700 also includes a communication device 746, which is configured to establish communication over the entire network. In some embodiments, when used in a local area networking environment or a wide area networking environment (such as the Internet), the computing device 700 is usually connected to the network through a network interface such as a wireless network interface 748. Other possible embodiments use other wired and / or wireless communication devices. For example, some embodiments of the computing device 700 include an Ethernet network interface or a modem for communicating over the entire network. In other embodiments, the communication device 746 is capable of short-range wireless communication. Short-range wireless communication is unidirectional or bidirectional short-range to medium-range wireless communication. Short-range wireless communication can be established according to various technologies and protocols. Examples of short-range wireless communication include radio frequency identification (RFID), near field communication (NFC), Bluetooth technology and Wi-Fi technology.

[0184] The computing device 700 typically includes at least some form of computer-readable media. Computer-readable media includes any available media that can be accessed by the computing device 700. By way of example, computer-readable media include computer-readable storage media and computer-readable communication media.

[0185] Computer-readable storage media include volatile and nonvolatile, removable and non-removable media implemented in any device configured to store information such as computer-readable instructions, data structures, program modules or other data. Computer-readable storage media include, but are not limited to, random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other storage technology, compact disk read-only memory, digital versatile disk or other optical storage, cassettes, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by the computing device 700.

[0186] Computer-readable communication media are typically embodied as computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and include any information delivery media. The term "modulated data signal" refers to a signal that has one or more of its characteristics set or changed in some way to encode the information in the signal. By way of example, computer-readable communication media include wired media, such as a wired network or direct wired connection; and wireless media, such as acoustic, radio frequency, infrared, and other wireless media. Combinations of any of the above are also included within the scope of computer-readable media.

[0187] In one aspect of the present disclosure, an automated clinical analyzer includes: a sample rack presentation unit configured to present a sample rack to a sample pipette, the sample rack having multiple sample container positions; an image capture device connected to the sample rack presentation unit, the image capture device configured to capture at least one image on at least one region of interest on the sample rack, the at least one region of interest including at least one sample container; a computing device configured to identify a sample container type in a captured image by matching the captured image with a reference image, the reference image including images of multiple different sample container types; and the computing device is further configured to identify the sample container type in the captured image based on a matching threshold with the reference image, wherein if the captured image exceeds or equals the threshold of the reference image, the sample container type is identified as the sample container type of the reference image, and wherein if the captured image does not meet the threshold of any reference image, the sample container type is not identified.

[0188] In some examples, if the sample container type is not identified, the sample container will not be processed by the sample pipette.

[0189] In some examples, if a sample container type is identified, the computing device is further configured to determine lid presence by analyzing a region of interest on the captured image, where coordinates of the region of interest are based on the identified sample container type.

[0190] In some examples, the computing device is configured to analyze the region of interest based on a matching threshold with a reference image of the identified sample container type.

[0191] In some examples, the computing device is configured to analyze the region of interest based on edge detection.

[0192] In another aspect of the present disclosure, a method for determining a type of a container in a rack is provided, the method comprising: capturing at least one image of at least a portion of a container and at least a portion of a rack using an image capture device, the container being held by the rack; finding a reference feature on the rack as a first image feature in at least one image; at least partially defining a first search area in at least one image based on a first relative position to the first image feature in at least one image; finding a first feature on the container within the first search area in at least one image as a second image feature in at least one image; calculating a first geometric property of the container based on a position of the second image feature in at least one image; and determining the type of the container by looking up the first geometric property of the container in a table.

[0193] In some examples, the reference feature is an edge of the stent.

[0194] In some examples, the first image feature in the at least one image is offset and spaced apart in relative position from the first search region in the at least one image.

[0195] In some examples, the first relative position is predetermined.

[0196] In some examples, the method may also include: at least partially defining a second search area in the at least one image based on a second relative position of the first image feature in the image; finding a second feature on the container within the second search area in the at least one image as a third image feature in the at least one image; finding a third feature on the container within the second search area in the at least one image as a fourth image feature in the at least one image; calculating a second geometric characteristic of the container based on a distance between the third image feature and the fourth image feature in the at least one image; and further determining the type of the container by looking up the second geometric characteristic of the container in a table.

[0197] In some examples, the first geometric characteristic of the container is a longitudinal characteristic of the container.

[0198] In some examples, the first geometric characteristic of the container indicates a length of the container.

[0199] In some examples, the second geometric characteristic of the container indicates an outer diameter of the container.

[0200] In some examples, the at least one image includes only a single image.

[0201] In some examples, the at least one image includes a plurality of images taken at a plurality of support positions.

[0202] In some examples, the rack holds a plurality of containers, and wherein the type of an additional container in the plurality of containers is determined by looking up a geometric property of the additional container in a table.

[0203] In another aspect of the present disclosure, a method for determining the type and identity of a container in a rack is provided, the method comprising: using an image capture device to capture at least one high exposure image of at least a portion of the container and at least a portion of the rack, the container being held by the rack; reading a code mark from at least one high exposure image to thereby determine the identity of the container in the rack; using an image capture device to capture at least one low exposure image of at least a portion of the container and at least a portion of the rack; finding a reference feature on the rack as an image feature in at least one low exposure image and / or at least one high exposure image; at least partially defining a search area in at least one low exposure image based on a relative position to a first image feature; finding a feature on the container within a first search area in at least one low exposure image as a second image feature; calculating a geometric property of the container based on the position of the second image feature; and determining the type of the container by looking up the geometric property of the container in a table.

[0204] In some examples, the code marking is a barcode.

[0205] In another aspect of the present disclosure, a method for determining whether a cover is on a container in a bracket is provided, the method comprising: capturing at least one image of at least a portion of a container and at least a portion of a bracket using an image capture device, the container being held by the bracket; finding a reference feature on the bracket as a first image feature in at least one image; at least partially defining a search area in at least one image based on a first relative position to the first image feature in at least one image; finding a feature on the container within the search area in at least one image as a second image feature in at least one image; defining a histogram area based on a second relative position of the second image feature in at least one image; and extracting histogram information from the histogram area to determine whether the cover is on the container.

[0206] In some examples, the histogram region is within the search region.

[0207] The various embodiments described above are provided by way of example only and should not be considered as limitations on the claims appended hereto. It will be readily understood by those skilled in the art that various modifications and changes may be made without following the exemplary embodiments and applications described herein without departing from the true spirit and scope of the following claims.

Claims

1. A method for determining the type of a container in a rack in an automated analyzer, the method comprising: capturing at least one image of at least a portion of the container and at least a portion of the holder using an image capture device, the container being held by the holder; finding a reference feature on the support as a first image feature in the at least one image; at least partially defining a first search area in the at least one image based on a first relative position to the first image feature in the at least one image; Finding a first feature on the container within the first search area in the at least one image as a second image feature in the at least one image; calculating a first geometric characteristic of the container based on a position of the second image feature in the at least one image; as well as determining the type of the container by looking up the first geometric property of the container in a table, Wherein the holder holds a plurality of containers, and wherein the type of an additional container of the plurality of containers is determined by looking up a geometric property of the additional container in a table.

2. The method according to claim 1, wherein: The reference feature is an edge of the bracket.

3. The method according to claim 1, wherein: The first image feature in the at least one image is offset and spaced apart from the first search region in the at least one image by the first relative position.

4. The method according to claim 1, wherein: The first relative position is predetermined.

5. The method according to claim 1, further comprising: at least partially defining a second search area in the at least one image based on a second relative position of the first image feature in the at least one image; Finding a second feature on the container within the second search area in the at least one image as a third image feature in the at least one image; Finding a third feature on the container within the second search area in the at least one image as a fourth image feature in the at least one image; calculating a second geometric characteristic of the container based on a distance between the third image feature and the fourth image feature in the at least one image; as well as The type of the container is further determined by looking up the second geometric characteristic of the container in the table.

6. The method according to claim 1, wherein: The first geometric characteristic of the container is a longitudinal characteristic of the container.

7. The method according to claim 1, wherein: The first geometric characteristic of the container is indicative of a length of the container.

8. The method according to claim 5, wherein: The second geometric characteristic of the container is indicative of an outer diameter of the container.

9. The method according to claim 1, wherein: The at least one image comprises only a single image.

10. The method according to claim 1, wherein: The at least one image includes a plurality of images taken at a plurality of support positions.

11. The method according to claim 6, further comprising: Determine the identity of the container in the rack by doing the following: capturing at least one high exposure image of at least a portion of the container and at least a portion of the support using an image capture device; as well as reading a code indicia from the at least one high exposure image to thereby determine an identity of the container in the rack; Wherein, the at least one image is at least one low-exposure image of at least a portion of the container and at least a portion of the support.

12. The method according to claim 11, wherein: The code is marked as a barcode.

13. The method according to claim 5, further comprising: determining whether a cap is on a container in said holder, wherein the histogram region is defined based on a second relative position of the second image feature in the at least one image; and Histogram information is extracted from the histogram area to determine whether the cap is on the container.

14. The method according to claim 13, wherein: The histogram area is within the search area.

15. The method according to claim 1, further comprising: adding a container of a new type to the table, and verifying the container of the new type; Enter information about the new type of container; loading the stent with a new type of container; loading a rack having containers of the new type into a loading channel of an automated analyzer; as well as The image capture device is used to observe characteristics of the new type of container.

Images