Compression quality variability based on the presence of a sample

Abstract

A method of reducing a digital storage size of an image of a sample for use in digital pathology operations is disclosed herein that can include receiving the image of the sample in a digital format; automatically dividing, by a computer processor, the image into multiple tiles that collectively form the image; and determining, by the computer processor and for each tile of the multiple tiles, a tile figure of merit (FOM) value depending on pixel intensities of pixels forming each tile. The method can further include comparing the tile FOM value for each tile to a first FOM threshold value and a second FOM value and digitally compressing the corresponding tile using one of a first compression quality, a second compression quality, and one a plurality of intermediate compression qualities depending on the tile FOM value.

Description

[0001] 2024-25225-P-WO-D0485-P15986WO01

[0002] COMPRESSION QUALITY VARIABILITY BASED ON THE PRESENCE OF A SAMPLE TECHNICAL FIELD

[0003] This disclosure is related generally to digital image compression and, more specifically, to selectively compressing portions of an image using different compression qualities to reduce a storage size of the compressed image.

[0004] BACKGROUND

[0005] Digital pathology operations can include capturing digital images of samples, such as organic tissue, under magnification and focused on high image clarity to allow for review by industry and medical professionals. Due to the nature of diagnostics, the capture of details of the sample by the image is of significant importance. However, one consequence of the images being magnified and / or at increased clarity is that a digital / electronic storage size of each image can be very large. For example, each image can be one gigabyte or larger. With digital pathology operations potentially resulting in the capture of hundreds or thousands of images, systems accommodating the digital pathology operations can require mass amounts of digital storage.

[0006] SUMMARY

[0007] A first example method of reducing a digital storage size of an image of a sample for use in digital pathology operations is disclosed herein that includes receiving the image of the sample in a digital format; automatically dividing, by a computer processor, the image into multiple tiles that collectively form the image; and determining, by the computer processor and for each tile of the multiple tiles, a tile figure of merit (FOM) value depending on pixel intensities of pixels that form each tile. The method can further include comparing the tile FOM value for each tile to a first FOM threshold value and a second FOM value and: 1) in response to the tile FOM value being equal to or greater than to the first FOM threshold value, determining with high certainty that the tile shows at least a portion of the sample and digitally compressing the corresponding tile using a first compression quality; 2) in response to the tile FOM value being less than or equal to the second FOM threshold value, determining with high certainty that the tile does not show at least a portion of the sample and digitally compressing the corresponding tile using a second compression quality that is lower than the first compression quality; and 3) in response to the tile FOM value being between the first FOM threshold value and the second FOM threshold value, digitally compressing the corresponding tile at one of a plurality ofintermediate compression qualities between the first compression quality and the second compression quality depending on the tile FOM value.

[0008] A second example method of reducing a digital storage size of an image of a sample for use in digital pathology operations is disclosed herein that includes obtaining the image in a digital format; automatically digitally dividing, by a computer processor, the image into multiple tiles that collectively form the image; and determining, by the computer processor and for each tile of the multiple tiles, a tile figure of merit (FOM) value depending on pixel intensities of pixels that form each tile. The method can further include automatically comparing the tile FOM value for each tile to a FOM threshold value with a tile FOM value that is above the FOM threshold being determined with high certainty to show at least a portion of the sample and a tile FOM value that is below the FOM threshold being determined with less certainty to show at least a portion of the sample; compressing, in response to the tile FOM value for the corresponding tile being equal to or greater than the FOM threshold value, the tile using a first compression quality; and compressing, in response to the tile FOM value for the corresponding tile being less than the FOM threshold value, the tile using a second compression quality that is lower than the first compression quality.

[0009] An example system of reducing a digital storage size of an image of a sample for use in digital pathology operations is disclosed herein that includes the image of the sample in a digital format, a parse module that functions at least partially in conjunction with a computer processor and is configured to automatically divide the image into multiple tiles that collectively form the image, a figure of merit (FOM) module that functions at least partially in conjunction with the computer processor and is configured to determine a tile FOM value for each tile depending on pixel intensities of pixels that form each tile, and a comparison module that functions at least partially in conjunction with the computer processor and is configured to automatically compare each tile FOM value to a first FOM threshold value and provide instructions to a compression module depending upon the comparison. The system can also include the compression module that functions at least partially in conjunction with the computer processor and is configured to, in response to the tile FOM value being equal to or greater than the first FOM threshold value, digitally compressing the corresponding tile using a first compression quality due to the comparison module determining with high certainty that the tile shows at least a portion of the sample and, in response to the tile FOM value being less than the first FOM threshold,digitally compressing the corresponding tile using a second compression quality that is lower than the first compression quality.

[0010] BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block schematic diagram of an example of a compression system. FIG. 2 is flow diagram of an example process for reducing a digital storage size of a digital image.

[0011] FIG. 3A is a perspective view of a sample on a slide.

[0012] FIG. 3B is a top down view of the sample on the slide.

[0013] FIG. 4 is a top down view of the digital image of the sample divided into tiles.

[0014] FIG. 5A is an enlarged view of tile 25A shown in FIG. 4 having the entire tile showing the sample.

[0015] FIG. 5B is an enlarged view of tile 25B shown in FIG. 4 having no portion of the tile showing the sample.

[0016] FIG. 5C is an enlarged view of tile 25C shown in FIG. 4 having a portion of the tile showing the sample.

[0017] FIG. 6A is a graph showing the figure of merit (FOM) value versus compression quality with a linear curve.

[0018] FIG. 6B is a graph showing the FOM value versus compression quality with a nonlinear curve.

[0019] While the above-identified figures set forth one or more examples of the present disclosure, other examples / embodiments are also contemplated, as noted in the discussion. In all cases, this disclosure presents the invention by way of representation and not limitation. It should be understood that numerous other modifications and embodiments can be devised by those skilled in the art, which fall within the scope and spirit of the principles of the invention. The figure may not be drawn to scale, and applications and examples of the present invention may include features and components not specifically shown in the drawings.

[0020] DETAILED DESCRIPTION

[0021] Systems and processes are disclosed herein for the reduction of a digital storage size of a digital image showing a sample during digital pathology operations. Additionally, the systems and processes decrease the time and / or resources needed for electronic / digital transmission of the digital image. The compression systems and methods digitally / electronically divide the image into multiple parts / pieces (referred to herein as“tiles”), determine if each tile shows a portion of the sample, and digitally compress each tile at a selected compression quality depending on the certainty that the tile shows a portion of the sample (which is reflected in a tile figure of merit value representative of the pixel intensities of the pixels that form the tile).

[0022] Whether and / or to what extent each tile shows a portion of the sample is determined, for example, by ascertaining the tile figure of merit (also referred to herein as the “FOM”) value for each tile, which is dependent upon the pixel intensities of the pixels that form the tile and is representative of whether the tile shows at least a portion of the sample. Then, the tile FOM value for each tile of the image is compared to a first FOM threshold value and / or a second FOM threshold value. For example, a tile FOM value above the first FOM threshold value represents a high certainty that the tile shows at least a portion of the sample, a tile FOM value below the second FOM threshold value represents a high certainty that he tile does not show the sample, and a tile FOM value between the first FOM threshold value and the second FOM threshold value represents at least some uncertainty as to whether the tile shows a portion of the sample.

[0023] In a first example, the systems and methods include only the first FOM threshold value, and the first FOM threshold value is set at a value in which any tile FOM value equal to or above the first FOM threshold value is certain to show at least a portion of the sample and any tile FOM values below the first threshold value is certain not to show any portion of the sample. Thus, any tiles that have corresponding tile FOM values that are equal to or above the first FOM threshold are compressed using a first compression quality (e.g., high-quality compression) while any tiles that have corresponding tile FOM values that are below the first FOM threshold value are compressed using a second compression quality (e.g., low-quality compression).

[0024] In a second example, the first and second threshold values are set at values in which any tile FOM values equal to or above the first FOM threshold value are certain to show at least a portion of the sample and any tile FOM values below or equal to the second FOM threshold value are certain not to show at least a portion of the sample. Thus, any tiles that have corresponding tile FOM values that are equal to or above the first FOM threshold value are compressed using the first compression quality (e.g., high-quality compression) and any tiles that have corresponding tile FOM values that are below or equal to the second FOM threshold value are compressed via the second compression quality (e.g., low-quality compression). In this example, any tiles that have tile FOM values between the first FOM threshold value and the second FOM threshold value are compressedat an intermediate compression quality that is between the first compression quality and the second compression quality to reflect uncertainty as to whether the corresponding tile shows a portion of the sample. The intermediate compression quality for these tiles is variable, dependent upon the corresponding tile FOM values, and can be determined using a curve showing the tile FOM values versus compression quality that is linear, exponential, decreases at a greater rate regarding the compression quality versus the tile FOM value as the tile FOM value is farther from the first FOM threshold value, based upon empirical and / or other data, and / or determined using another curve and / or method.

[0025] The compression systems and methods can be configured to automatically digitally parse / divide the digital image (upon capture and / or reception of the image) into the multiple tiles, automatically determine the tile FOM values for each of the tiles concurrently and / or sequentially, automatically compare each tile FOM value to the threshold value(s) concurrently and / or sequentially, and / or automatically compress each tile at a compression quality dependent upon the corresponding tile FOM value concurrently and / or sequentially. Further, the compression systems and methods can be configured to transmit, via a network connection and / or other p, the compressed tiles / image to a storage server having storage media.

[0026] The disclosed compression systems and methods allow for the determination of a certainty that the tiles include / show at least a portion of the sample and, depending upon the certainty (and / or uncertainty), selectively and variably compress the tiles. This variability in compression quality of the tiles that form the image allows for an increased clarity of tiles that show the sample so that the finer details shown in those tiles can be more easily viewed and used for diagnostic purposes. Similarly, the compression systems and methods allow for selectively and variably compressing portions of the image at a lower quality if that portion / tile does not show a portion of the sample, thereby saving digital storage space without negatively impacting the diagnostic capabilities of the compressed image. Thus, the image as a whole is compressed in such a way that the image has a smaller digital storage size (as compared to the uncompressed image) while also showing the sample in higher clarity. These and other advantages will be realized by reviewing the below disclosure.

[0027] FIG. 1 is a block schematic diagram of example compression system 10. Compression system 10 (also referred to herein as just “system 10”) can receive, obtain, and / or otherwise access uncompressed image 16A from imaging machine 12 and send, save, and / or otherwise provide compressed image 16B (having compressed tiles 35) tostorage server 13 with storage media 14. While described herein as being separate and distinct components from system 10, imaging machine 12 and / or storage server 13 can be incorporated into and / or otherwise function in conjunction with system 10 (and vice versa) such that system 10, imaging machine 12, and / or storage server 13 are physically and / or digitally / electronically contained within the same structure, hardware, and / or software.

[0028] Compression system 10 can include processor 20, storage media 22, user interface 24, parse module 26, FOM module 28, comparison module 30, and compression module 32. Storage media 22 can include uncompressed image 16A made up of example uncompressed tiles 25A, 25B, and / or 25C (also herein referred to as “tiles 25”) and / or compressed image 16B made up of compressed tiles 35. In FIG. 1, storage media 22 is shown to store uncompressed tiles 25A-25C before the uncompressed tiles 25 of uncompressed image 16A are compressed to form compressed image 16B having compressed tiles 35, which is then transmitted via network connection 34 to storage server 13 having storage media 14. As described herein, storage media 22 and storage media 14 can be the same or different storage media, software, and / or hardware. System 10 can include other components not expressly disclosed herein.

[0029] Any of the components / systems shown in FIG. 1 can communicate with each other via any type of wired and wireless communication, including via the use of the internet and / or via network connection 34. In one example, the components / systems shown and described herein can communicate via a publisher / subscriber message bus and / or similar configurations.

[0030] Compression system 10 (and process 100 described with regards to FIG. 2) can include other steps, components, modules, configurations, and / or features not expressly disclosed herein that are suitable for compressing image 16A. For example, system 10 can include any number of digital / electronic storage media (storage media 14 and / or 22) for storing data, information, and / or executable instructions. System 10 can include any number of computer processors (e.g., processor 20) for performing tasks / instructions with regards to system 10 and / or process 100. Further, system 10 can allow for communication via wired or wireless communication methods between components of system 10 and / or between other components, systems, individuals / users, etc. distant from system 10. System 10 is described herein as including one or multiple “modules,” which can be any hardware and / or software for performing the tasks, functionality, and / or capabilities described herein. These “modules” can be instantiated in dedicated hardware and / or software, and / or can be defined functionally and use shared hardware and / or software.Additionally, system 10 can be a discrete assembly or be formed by one or more components capable of individually or collectively implementing the functionalities described herein. In some examples, system 10 can be implemented as a plurality of discrete circuitry subassemblies. One or all components of system 10 can be considered to form a single computing device even when distributed across multiple component computing devices. System 10 can include a configuration in which one, some, or all of the functions described herein are performed by different components. System 10 can include various components for performing the above functions (as well as other functions described in this disclosure), such as processor 20, storage media 14 and / or 22, and / or user interface 24.

[0031] FIG. 1 focuses on hardware components of compression system 10, and is provided as an illustrative example of a general hardware system for performing the capabilities discussed herein. The components presented in FIG. 1, particularly including modules 26, 28, 30, and / or 32 can be omitted or replaced with analogous hardware and / or software in different architectures without departing from the scope and spirit of the present disclosure.

[0032] The components of FIG. 1 are described herein with regards to example sample 50 shown on slide 52 (and between cover slip 54 and slide 52) in FIGS. 3A and 3B; uncompressed image 16A showing sample 50 in FIG. 4; uncompressed tiles 25A-25C (collectively referred to herein as “tiles 25’") in FIGS. 5A-5C, respectively; and graphs 70A and 70B in FIGS. 6A and 6B, respectively.

[0033] Imaging machine 12 can be any machine, system, hardware, and / or software configured to capture uncompressed image 16A of sample 50. Shown in FIGS. 3A and 3B, sample 50 can be located between, for example, slide 52 and cover slip 54. The example uncompressed image 16A as captured by imaging machine 12 is shown in FIG. 4 (with uncompressed image 16A being divided into tiles 25 by compression system 10 described below). Sample 50 can be organic tissue and / or any material, substance, etc. for which imaging and / or diagnostics is desired. Generally, slide 52 and cover slip 54 are transparent to allow for optimal viewing and imaging of sample 50 therebetween. For example, slide 52 and / or cover slip 54 are clear glass and / or plastic with sample 50 pressed and held in place therebetween. Imaging machine 12 can be configured to manually and / or automatically capture multiple uncompressed images 16A of sample 50 and / or of other samples in quick succession.Imaging machine 12 can be a digital scanner that acquires / captures uncompressed image 16A of the entire slide 52, known in the industry as a while slide image (also referred to herein as a “WSI”) and save the WSI (e.g., the uncompressed image 16A) as a digital image data file in a partially or entirely automated process that does not need human / pathologist intervention. The digital image data file can be stored in a database that is available, for example, via a network to allow for viewing and diagnostics by a pathologist at a workstation. The workstation can have further capabilities for viewing, modifying, annotation, etc. via, for example, a visualization application and / or other hardware and / or software. Both uncompressed images 16A and compressed images 16B as described herein can be WSIs.

[0034] In one example, imaging machine 12 can be configured to capture images (e.g., WSIs) of dozens or hundreds of samples on differing slides sequentially without human / pathologist intervention / interaction. Then, those uncompressed images 16A can be provided / sent to, obtained by, and / or otherwise accessed by compression system 10. Sample 50, along with slide 52 and cover slip 54, can have other configurations than that shown in FIGS. 3 A and 3B, such as a configuration in which slide 52 and / or cover slip 54 are absent, sample 50 is not generally flat, and / or other configurations. The configurations, characteristics, and functionalities of imaging machine 12 as well as sample 50 with slide 52 and cover slip 54 are known to one of skill in the industry. For example, sample 50 can be dyed and / or otherwise manipulated to allow for easier capture and / or viewing of sample 50 in uncompressed image 16A (e.g., WSI). In another example, uncompressed image 16A (e.g., WSI) can be captured using any type of light and / or photographic technique, such as using infrared light, visible light, ultraviolet light, wide shot, magnification, polarization, color correction, greyscale, and / or other techniques / lighting.

[0035] Sample 50 as imaged / captured in uncompressed image 16A shown in FIG.

[0036] 4, for example, is not shaped to match the shape of the area shown uncompressed image 16A. In FIG. 4, for example, uncompressed image 16A is rectangular while sample 50 is an irregular shape with the edges of sample 50 being differently shaped and distant from the edges of uncompressed image 16A. Thus, there is blank space between sample 50 and the edges of uncompressed image 16A within which sample 50 is absent. Even though this blank space does not show sample 50, the clarity of the blank space in uncompressed image 16A is usually similar to the increased clarity of portions of uncompressed image 16A showing sample 50. As described below, the increased clarity, and thus the high digital storage space required, is not necessary for viewing and / or diagnostics of sample 50because the blank space does not show sample 50 in uncompressed image 16 A. Therefore, compression system 10 identifies the blank space in uncompressed image 16A and compresses the tiles 25 that show the blank space at a lower quality (so thus at a decreased storage requirement) compared to the compression quality of the portions of uncompressed image 16A that show sample 50.

[0037] System 10 can exchange information, such as compressed image 16B and / or compressed tiles 35, with storage server 13 that is, for example, distant from system 10. System 10 can be in communication with storage server 13 via network connection 34 and / or via a Digital Imaging and Communications in Medicine (DICOM) communications protocol to transmit image 16B and / or compressed tiles 35 before, during, and / or after saving compressed image 16B and / or compressed tiles 35 to storage media 14 and / or 22. Thus, system 10 and storage server 13 can be configured to be on the same centralized and / or distributed server system to allow for the exchange of information via network connection 34. Other characteristics, capabilities, and / or functionalities of compression system 10 are described below. For example, system 10 and / or process 100 (described with regards to FIG. 2) can use any protocols, formats, files, standards, etc. set out in D1C0M as known to one in the industry.

[0038] Uncompressed image 16A can be in any format suitable for showing sample 50 and / or for capture by imaging machine 12. In one example, uncompressed image 16A is in Joint Photographic Experts Group (JPEG) format and is compressed by compression system 10 using JPEG compression. In another example, uncompressed image 16A is in Digital Imaging and Communications in Medicine (DICOM) format / standards and can use the compression recommended in DICOM protocols / standards, such as JPEG 2000 compression. Uncompressed image 16A can be in any other format. The compression used by system 10 to reduce the digital storage side of image 16 (e.g., to generate compressed image 16B) can be any compression suitable for compressing uncompressed image 16A (i.e., tiles 25) to reduce the digital storage size and / or to maintain increased visual clarity of sample 50 to allow for viewing and / or diagnostics. In one example, the compression used by system 10 is JPEG compression. In another example, the compression used is JPEG2000 compression. Uncompressed image 16A can be compressed using lossy and / or lossless compression and / or depending on the desired visual clarity, digital storage size reduction, and / or other needs.

[0039] System 10 (and / or the components of system 10) can include one or multiple computer / data processors 20 (also referred to herein as “processor 20”). In general,processor 20 can include any or more than one of a processor, a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other equivalent discrete or integrated logic circuitry. Processor 20 can perform instructions stored within storage media 14 and / or 22 (or located elsewhere), and / or processor 20 can include memory such that processor 20 is able to store instructions and perform the functions described herein. Additionally, processor 20 can perform other computing processes described herein, such as the functions performed by any of the components of system 10, including modules 26, 28, 30, and / or 32.

[0040] System 10 (and / or the components of system 10) can also include storage media 14 and / or 22. Storage media 14 and / or 22 are configured to store information and, in some examples, can be described as a computer-readable storage medium, media, and / or memory. In some examples, a computer-readable storage medium can include a non-transitory medium. The term “non-transitory” can indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium can store data that can, over time, change (e.g., in RAM or cache). In some examples, storage media 14 and / or 22 are temporary memory. As used herein, a temporary memory refers to a memory having a primary purpose that is not long-term storage. Storage media 14 and / or 22, in some examples, are described as volatile memory. As used herein, a volatile memory refers to a memory that that the memory does not maintain stored contents when power to storage media 14 and / or 22 are turned off. Examples of volatile memories can include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories. In some examples, the storage media / memory is used to store program instructions for execution by the processor. The memory, in one example, is used by software or applications running on system 10 to temporarily store information during program execution.

[0041] Storage media 14 and / or 22 can be configured to store larger amounts of information than volatile memory. Storage media 14 and / or 22 can further be configured for long-term storage of information. In some examples, storage media 14 and / or 22 include non-volatile storage elements. Examples of such non-volatile storage elements can include, for example, magnetic hard discs, optical discs, floppy discs, flash memories, cloud storage media, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Additionally, storage media 14 and / or 22 can bedigital / electronic storage in the “cloud” that is distant from storage server 13 and / or the other components of system 10.

[0042] System 10 can also include user interface 24. User interface 24 can be an input and / or output device and enables an operator / user to control operation, perform modifications, and / or view data and / or specifications as well as view uncompressed image 16A, uncompressed tiles 25, compressed image 16B, compressed tiles 35, and / or the other systems / components within system 10 and / or in communication with system 10. For example, user interface 24 can be configured to receive inputs from a user / system and / or provide outputs. User interface 24 can include one or more of a sound card, a video graphics card, a speaker, a display device (e.g., a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, etc.), a touchscreen, a keyboard, a mouse, a joystick, and / or other type of device for facilitating input and / or output of information in a form understandable to users and / or machines. In one example, user interface 24 can be configured to display compressed image 16B made up of compressed tiles 35.

[0043] Compression system 10 can include parse module 26, which can include and / or function in conjunction with any of the other components of system 10 (such as processor 20, storage media 22, and / or user interface 24). Parse module 26 can be configured to divide / parse uncompressed image 16A into multiple uncompressed tiles 25 (shown in FIGS. 4 and 5A-5C as tiles 25A, 25B, and 25C) with the multiple tiles 25 collectively forming uncompressed image 16 A. Parse module 26 can divide uncompressed image 16A into any number, size, shape, organization, configuration, etc. of tiles 25. With regards to parse module 26, the “size” of each tile 25 is the physical size of the tile 25, which can be measured in pixels, inches, centimeters, etc. It is important to distinguish this “physical size” of the tile 25 from the “digital storage size” of the tile 25, which is the measurement of how much data the digital file of the tile 25 consumes / contains.

[0044] In the example shown in FIG. 4, uncompressed image 16A is divided into tiles 25 that are squares of similar physical size and shape with the tiles 25 extending in rows and columns across the entirety of uncompressed image 16A. While each tile 25 can have any physical size, in the example shown in FIG. 4, each tile 25 is square and has a width of 256 pixels and a height of 256 pixels. However, tiles 25 can be different physical sizes and shapes within the same uncompressed image and / or from one image to another image. For example, one tile 25 in an uncompressed image can be a polygon having many sides while another tile in the same uncompressed image can be a quadrilateral. In a secondexample, all tiles 25 in another uncompressed image are rectangular with two sides that are longer than the other two sides. In a third example, each tile 25 has approximately the same number of pixels but with some or all tiles 25 having different shapes from one another within the uncompressed image. While shown as having a physical size of 256 pixels by 256 pixels, each tile 25 can be larger or smaller depending on many factors, including the tile FOM value determinations, the type of compression that is to be used, the type / format of uncompressed image 16 A, the type and / or configuration of imaging machine 12, the characteristics of sample 50 (such as physical size, digital storage size, shape, color profile, etc.), the characteristics of uncompressed image 16A (such as physical size, digital storage size, shape, color profile, etc.), and / or other factors. Because uncompressed image 16A can be quite large (both physically and digitally), uncompressed image 16A can be divided into a large number of tiles 25. In one example, uncompressed image 16A can be divided into more than one-hundred tiles 25. In other examples, uncompressed image 16A can be divided into thousands or more tiles 25. Parse module 26 can divide uncompressed image 16A into tiles 25 with consideration as to whether sample 50 is shown in any of the tiles 25. For example, the tiles 25 that show sample 50 can be a different physical size, shape, etc. than each other and / or to the tiles 25 that do not show sample 50. In this example, the tiles 25 that show sample 50 can have a physical size that is smaller than the tiles 25 that do not show sample 50.

[0045] Parse module 26 can be configured to manually divide uncompressed image 16A into tiles 25 as performed and / or initiated by a user, and / or parse module 26 can be configured to automatically divide uncompressed image 16A into tiles 25 in response to, for example, the reception of and / or access to uncompressed image 16A and / or in response to any other prompt / triggering events / instructions. For example, parse module 26 can automatically divide uncompressed image 16A into tiles 25 in response the reception of uncompressed image 16A from imaging machine 12 and automatically save uncompressed image 16A and / or tiles 25 to storage media 22 after performing the division. Parse module 26 can be configured to divide multiple images into multiple tiles simultaneously and / or sequentially such that compression system 10 can compress multiple images 16A at the same time and / or in series.

[0046] Tiles 25 that are created by parse module 26 via the division of uncompressed image 16A can fall into one of three categories, shown in FIGS. 5A-5C. FIG 5A shows tile 25A having the entirety of the image of tile 25A including sample 50A. As shown in FIG. 4, tile 25A is located in the interior of sample 50 so thus the entire tile 25Ashows a portion of sample 50 (shown as sample 50A in FIG. 5 A). As described with regards to FIGS. 6A and 6B, the tile FOM value for tile 25 A is greater than the first FOM threshold value representing a high certainty that tile 25A shows sample 50A. Thus, tile 25A is compressed at the first, highest compression quality to allow for viewing / diagnostics of sample 50A at the highest clarity (as described below).

[0047] FIG. 5B shows tile 25B having no portion of the image of tile 25B including any portion of sample 50. As shown in FIG. 4, tile 25B is located in the blank space between the edge of sample 50 and the edge of uncompressed image 16A. Thus, as shown in FIG.

[0048] 5B, tiles 25B is blank and only shows the background (e.g., slide 52 and / or the background of slide 52 if slide 52 is transparent). Tile 25B is not useful in viewing / diagnostics of sample 50. The tile FOM value for tile 25B is less than the second FOM threshold value representing a high certainty that tile 25B shows no portion of sample 50. Thus, tile 25B is compressed at the second, lowest compression quality to allow for a decrease in digital storage size of tile 25B (as described below).

[0049] FIG. 5C shows tile 25C having only a portion of the image of tile 25C including a portion of sample 50C. As shown in FIG. 4, tile 25C is located along the edge of sample 50 so thus only a portion of tile 25B shows a portion of sample 50 (shown as sample 50B in FIG. 5C). In other examples, tile 25C can be located within an interior of sample 50 but still show only a portion of sample 50 due to sample 50 containing a void and / or tile 25C can show the entirety of sample 50 such that tile 25C shows both sample 50 and some blank space. As described with regards to FIGS. 6 A and 6B, the tile FOM value for tile 25C is between the first FOM threshold value and the second FOM threshold value representing a low certainty (e.g., uncertainty) as to whether tile 25C shows any portion of sample 50. Thus, tile 25C is compressed at a compression quality that is between the first, highest compression quality and the second, lowest compression quality depending on the tile FOM value (as described below).

[0050] Compression system 10 can include FOM module 28, which can include and / or function in conjunction with any of the other components of system 10 (such a processor 20, storage media 22, and / or user interface 24). FOM module 28 can be configured to, after parse module 26 divides uncompressed image 16A into tiles 25, determine the tile figure of merit (FOM) value for each tile 25. The tile FOM value can be dependent upon the pixel intensities of the pixels that form each corresponding tile 25 such that the tile FOM value can be representative of whether at least a portion of the tile 25 shows at least a portion of the sample 50. With each tile 25 being a combination of pixels(that form the portion of image 16A shown in tile 25), the pixel intensity of each pixel can be a brightness or color value of each pixel in the portion of image 16A forming tile 25. Pixel intensity is a term known to one of skill in the industry. The pixel intensity for each pixel can be a value between, for example, 0 and 255 and can be determined by FOM module 28 (and / or processor 20) for each pixel in each tile 25 and then used collectively to determine the file FOM value for each corresponding tile 25. Such determinations of the pixel intensities can be performed concurrently for all pixels that form tile 25 and / or for all tiles 25 of uncompressed image 16A. In another example, the pixel intensity is a characteristic of each pixel and is previously saved as a property associated with the pixel before use by compression system 10.

[0051] In one example, the tile FOM value can be a value representative of the differences in pixel intensities between neighboring pixels in tile 25 (with a greater difference in pixel intensities between pixels close to one another implying that tile 25 shows at least a portion of sample 50). In another example, the tile FOM value can be representative of the differences in pixel intensities from the darkness of the pixels (pixels that likely show sample 50) in comparison to bright pixels that do not show sample 50 (which may show the light background instead of the dark sample 50). Thus, the tile FOM value, as determined by FOM module 28, is an indicator as to whether tile 25 shows sample 50 and to what extent sample 50 is shown in tile 25 (e.g., the entirety of tile 25 A shows sample 50A, none of tile 25B shows sample 50, and a portion of tile 25C shows sample 50C).

[0052] The tile FOM value, which uses the pixel intensity for each pixel in the corresponding tile 25, can be determined using the following equation:

[0053]

[0054] with each tile 25 including a width along an arbitrary x-axis and a height along the corresponding y-axis such that p is the pixel intensity for each pixel px.ywithin the tile 25. The tile FOM value can be normalized to be a value between 0 and 1 and / or, in other examples, can be any value as long as the first FOM threshold value and / or second FOM threshold value are set accordingly. In the example shown in FIGS. 6 A and 6B, each tile FOM value is between 0 and 20 with a higher value representative of a tile 25 that shows at least a portion of sample 50 and a lower value representative of a tile 25 that shows noportion of sample 50. Thus, as described in more detail with regards to FIGS. 6A and 6B, the first FOM threshold value is set at around 15 to represent that a tile FOM value above the first FOM threshold value has a high certainty of showing sample 50 and the second FOM threshold value is set at around 5 to represent that a tile FOM value below the second FOM threshold value has a high certainty of showing no sample 50.

[0055] FOM module 28 can, after the determination of pixel intensities and / or tile FOM values, save the pixel intensities and / or tile FOM values to, for example, storage media 22 and / or another location for use by compression system 10 (e.g., comparison module 30). FOM module 28 also can be configured to manually determine the pixel intensities and / or tile FOM values for each pixel / tile 25 as performed by and / or initiated by a user, and / or FOM module 28 can be configured to automatically determine pixel intensities and / or tile FOM values in response to, for example, the division of uncompressed image 16A into tiles 25 by parse module 26 and / or in response to any other prompt / triggering events / instructions. FOM module 28 can be configured to determine pixel intensities and / or tile FOM values for multiple images 16A, tiles 25, and / or pixels simultaneously and / or sequentially such that the tile FOM values can be determined for multiple tiles 25 and / or on multiple images 16A at the same time and / or in series. In one example, uncompressed image 16A is divided into hundreds of tiles 25 and FOM module 28 automatically determines the tile FOM value for each of the hundreds of tiles 25 simultaneously and / or within a short amount of time (e.g., seconds).

[0056] Compression system 10 can include comparison module 30, which can include and / or function in conjunction with any of the other components of system 10 (such as processor 20, storage media 22, and / or user interface 24). Comparison module 30 can be configured to, after the determination of the tile FOM values by FOM module 26, compare each tile FOM value corresponding to each tile 25 of uncompressed image 16A to the first FOM threshold value and / or the second FOM threshold value.

[0057] The first FOM threshold value and the second FOM threshold value can be set at any value with the first FOM threshold value representative of a FOM value in which the corresponding tile is certain to have at least a portion of the tile showing at least a portion of sample 50 and the second FOM threshold value representative of a FOM value in which the corresponding tile is certain to show none of sample 50. The first FOM threshold value and / or the second FOM threshold value can be modified from tile to tile and / or from image to image depending upon a variety of factors, including the background brightness / color of uncompressed image 16A, the brightness / color of sample 50, theformat / type of uncompressed image 16A, the level of certainty / uncertainty a user desires, and / or other factors.

[0058] In another example, the system 10 and / or processes can include only the first FOM threshold value with the first FOM threshold value being set such that a tile FOM value equal to or greater than the first FOM threshold value corresponding to a tile 25 having a high certainty of at least a portion of the tile showing at least a portion of sample 50 and a tile FOM value less than the first FOM threshold value corresponding to a tile 25 having a high certainty of no portion of the tile showing no portion of sample 50. Other examples can include having more than two threshold values and / or different configurations of threshold values. As previously mentioned, the example shown herein includes a first FOM threshold value set at around 15 and a second FOM threshold value set at around 5. In another example, each tile FOM value is normalized and the first FOM threshold value is around 0.75 and the second FOM threshold value is around 0.25.

[0059] The first FOM threshold value and / or the second FOM threshold value can be manually and / or automatically set for each and / or all tiles 25 and / or each and / or all uncompressed images 16A by comparison module 30 and / or by any other components of system 10. Comparison module 30 can, depending on the comparison to first FOM threshold value and / or second FOM threshold value, put each tile 25 corresponding to each tile FOM value into one of three categories (shown in FIGS. 6A and 6B): 1) a first, high compression quality corresponding to a tile FOM value that is equal to or greater than the first FOM threshold value; 2) a second, low compression quality corresponding to a tile FOM value that is less than or equal to the second FOM threshold value; and 3) an intermediate compression quality corresponding to a tile FOM value that is between the first FOM threshold value and the second FOM threshold value. As described with regards to compression module 32, the intermediate compression quality is variable, between the first compression quality and the second compression quality, and dependent upon the tile FOM value. Comparison module 30 can provide instructions to compression module 32 regarding the compression to use on each tile 25.

[0060] Comparison module 30 and, for example, user interface 24 can be configured to modify the values of the first FOM threshold value and / or the second FOM threshold value depending on the certainty / uncertainty the user desires with regards to tile 25 potentially showing a portion of sample 50. For example, if the user desires to ensure that all tiles 25 that show any portion of sample 50 are compressed at a higher compression quality, the first FOM threshold value and / or the second threshold value can be set at alower value than that shown in FIGS 6 A and 6B, such as 10 for the first FOM threshold value and 2 for the second FOM threshold value. Such a configuration would result in a greater digital storage size of compressed image 16B but would ensure that all or almost all of sample 50 is shown in increased clarity. The FOM threshold values can be set / designated at other values depending on a variety of factors, including the certainty / uncertainty of the user, the balance between digital storage space reductions and high clarity requirements, and / or the characteristics of sample 50 (which may require different threshold values to accurately categorize tiles 25).

[0061] Comparison module 30 can, after comparing each tile FOM value to the FOM threshold values, save the results of the comparison (e.g., a designation of the category / compression each tile 25 is to be compressed into / by) to, for example, storage media 22 and / or another location for use by compression system 10 (e.g., compression module 32). Comparison module 30 can be configured to manually compare each tile FOM value to the FOM threshold value(s) as performed by and / or initiated by a user, and / or comparison module 30 can be configured to automatically compare each tile FOM value to the FOM threshold value(s) in response to, for example, the determination of the tile FOM value(s) and / or in response to any other prompt / triggering events / instructions. Comparison module 30 can be configured to compare the tile FOM value(s) to the FOM threshold value(s) for multiple images 16A and / or tiles 25 simultaneously and / or sequentially such that the comparison can be made for multiple tiles 25 and / or multiple images 16A at the same time and / or in series. In one example, uncompressed image 16A is divided into hundreds of tiles 25 and comparison module 30 automatically compares the tile FOM value for each of the hundreds of tiles 25 to the FOM threshold value(s) simultaneously and / or within a short amount of time (e.g., seconds).

[0062] Compression system 10 can include compression module 32, which can include and / or function in conjunction with any of the other components of system 10 (such as processor 20, storage media 14 and / or 22, and / or user interface 24). Compression module 32 can be configured to digitally compress one, multiple, or all tiles 25 depending on the tile FOM value in comparison to the first FOM threshold value and / or the second FOM threshold value. For example, compression system 10 can be configured to, in response to the tile FOM value being equal to or greater than the first FOM threshold value, digitally compress the corresponding tile 25 (e.g., tile 25 A) using the first, high compression quality. Further, compression module 32 can be configured to, in response to the tile FOM value being less than or equal to the second FOM threshold value, digitally compress thecorresponding tile 25 (e.g., tile 25B) using the second, low compression quality. Additionally, compression module 32 can be configured to, in response to the tile FOM value being between the first FOM threshold value and the second FOM threshold value, digitally compress the corresponding tile 25 (e.g., tile 25C) at one of a plurality of intermediate compression qualities between the first, high compression quality and the second, low compression quality.

[0063] The determination of at which quality to compress a tile 25 that has a corresponding tile FOM value between the first FOM threshold value and the second FOM threshold value can be performed, by compression module 32, using an equation and / or graph / curve, such as graph 70A shown in FIG. 6A and / or graph 70B shown in FIG. 6B. The FOM values and / or compression qualities shown in FIGS. 6 A and 6B (and the curves / graphs representing the relationships therebetween) are provided merely as examples and other values and / or qualities can be used depending on a variety of factors, such as whether the FOM values are normalized, the background brightness / color of uncompressed image 16A, the brightness / color of sample 50, the file format of uncompressed image 16A, the compression type / format used by system 10, the amount (e.g., to what extent) of digital storage space reduction desired, the visual clarity requirements / desires of compressed image 16B, and / or other factors.

[0064] Graph 70A in FIG. 6A and graph 70B in FIG. 6B show the compression quality versus FOM value with the compression quality being, for example, shown as JPEG quality factors that range from 100 (uncompressed) to 0 (maximum compression). Generally, the compression of an image using JPEG quality factors ranges from 90, which is considered high quality, to 70, which is considered lower quality as compared to the higher quality factor / compression. Thus, the example graph 70A in FIG. 6A and the example graph 70B in FIG. 6B show the compression quality ranging from 90 (the first, highest compression quality) to 70 (the second, lowest compression quality). However, if another type / format of compression is used, such as the compression typically used in DICOM protocols / standards / format (e.g., JPEG 2000 compression), the tile FOM value as well as the threshold values can be normalized and / or modified to accommodate the disclosed system / method such that a comparable compression quality metric can be derived and used herein. For example, the first compression quality can be set at any value that represents high quality compression and the second compression quality can be set at any value that represents lower quality compression. In example graphs 70A and 70B, the FOM value ranges from 0 to 20. With both graphs 70A and 70B, for example, the first FOMthreshold value is set at 15 and the second FOM threshold value is set at 5 while the first, highest compression quality is set at 90; the second, lowest compression quality is set at 70; and the intermediate quality is along a monotonic curve therebetween.

[0065] In graph 70A shown in FIG. 6A, the intermediate compression quality follows a linear curve decreasing from the compression quality of 90 at a tile FOM value of 15 to the compression quality of 70 at a tile FOM value of 5. The tile FOM value corresponding to tile 25 A, which has the entirety of the tile 25 showing sample 50, is at an FOM value of around 18 so thus is compressed at the first, highest compression quality. This represents a high certainty that tile 25A shows sample 50A and ensures that sample 50A shown in tile 25A is compressed at the highest quality to allow for viewing / diagnostics of sample 50A at increased visual clarity. The tile FOM value corresponding to tile 25B, which has no portion of the tile 25 showing sample 50, is at an FOM value of around 3 so thus is compressed at the second, lowest compression quality. This represents a high certainty that tile 25B shows no portion of sample 50 and results in tile 25B being compressed at the lowest quality to provide the greatest digital storage space reduction while not affecting the viewability / clarity of sample 50 (because tile 25B does not show any portion of sample 50). The tile FOM value corresponding to tile 25C, which has a portion of the tile 25C showing a portion of sample 50C, is at an FOM value around 11. The tile FOM value being between the first FOM threshold value and the second FOM threshold value represents some uncertainty as to whether tile 50C shows any of sample 50C. To account for this uncertainty, tile 25C is compressed at an intermediate compression quality determined by the linear curve shown in graph 70A. In graph 70A, this intermediate compression quality is around 80 to account for the uncertainty while still providing the tile 25C at increased clarity (to allow for increased viewability of the portion of sample 50C in tile 25C) and reducing digital storage space. However, as shown in graph 70B in FIG. 6B (and described below), the curve determining the intermediate compression quality can be adjusted to provide for certainty / uncertainty considerations.

[0066] Example graph 70B shown in FIG. 6B is similar to graph 70A except that the intermediate compression quality decreases at a greater rate regarding the compression quality versus the tile FOM value as the tile FOM value is farther from the first FOM threshold value. In FIG. 6B, this intermediate compression quality of each tile 25 having the tile FOM value that is between the first FOM threshold value and the second FOM threshold value is determined using the following equation:

[0067]

[0068] with qmax being the first compression quality for a tile FOM value that is equal to or greater than the first FOM threshold value, qminbeing the second compression quality for a tile FOM value that is less than or equal to the second FOM threshold value, ampie being the first FOM threshold value, tdear being the second FOM threshold value, and 5 being the particular tile FOM value for which the compression quality is being determined. As mentioned above, the curve / equation ensures that, even if there is some uncertainty as to whether tile 25C shows sample 50C, tile 25C is compressed at a higher quality (so thus has increased visual clarity) as compared to graph 70A. Such a configuration may be desired if the edges of sample 50 are of increased importance and / or to prevent a large decrease in clarity from a tile 25A that has high certainty of the tile showing sample 50A to a neighboring tile 25C that is on the edge of sample 50 and has a lower certainty of showing sample 50C. This configuration also balances the desired to decrease the digital storage size of tile 25C while also showing sample 50C at an increased clarity.

[0069] Compression module 32 can be configured to manually digitally compress tiles 25 to create compressed tile 35 of compressed image 16B as performed by and / or initiated by a user, and / or compression module 32 can be configured to automatically digitally compress tiles 25 in response to, for example, the determination by comparison module 30 regarding the compression quality at which each tile 25 will be compressed and / or in response to any other prompt triggering events / instructions. Compression module 32 can be configured to digitally compress multiple tiles 25 from multiple uncompressed images 16A simultaneously and / or sequentially such that the compression can be performed at the same time and / or in series. In one example, uncompressed image 16A is divided into hundreds of tiles 25 and compression module 32 automatically digitally compresses each of the hundreds of tiles 25 to generate compressed tiles 35 that collectively form compressed image 16B with the tiles 25 being compressed simultaneously and / or within a short amount of time (e.g., seconds).

[0070] Compression module 32 can, after compressing one, multiple, or all tiles 25, digitally / electronically transmit compressed tiles 35 and / or compressed image 16B via network connection 34 to storage server 1 . Concurrently and / or thereafter, compressed tiles 35 and / or compressed image 16B can be saved to storage media 14. Due to tiles 25 / 35 being compressed individually, the digital storage size of compressed image 16B is smaller while sample 50 is shown in an increased clarity as compared to an image in which thesame compression quality is applied across the entirety of the image regardless of the size, placement, etc. of the sample and / or as compared to an image in which no compression is performed. System 10 allows for a balance between digital storage size reduction of image 16B and increased clarity of sample 50. System 10 can be used with any image file / format and / or compression format / process, including those compression files / formats that do not support storing tiles 25 (thus, system 10 can be configured to divide, store, and / or otherwise record the location of each tile 25) and / or those compression files / formats for which a comparable compression quality metric is not standard / inherent but can be derived herein. Further, with the reduction in digital storage size of compressed image 16B, the transmission, upload, and / or download speed of compressed image 16B is increased, thereby maximizing resource savings while providing efficiency. System 10 can include other capabilities, configurations, functionalities, and advantages than those detailed herein. A process that includes the selective compression of tiles 25 of uncompressed image 16A as described with regards to system 10 and FIGS. 1 and 3A-6B is described with regards to FIG. 2 below.

[0071] FIG. 2 is an example method flow chart describing process 100 for reducing a digital storage size and increasing the transmission speed of an image, such as uncompressed image 16A described with regards to system 10. While process 100 is described herein as being used with regards to compression system 10, process 100 can be performed by any system(s) having any components, capabilities, configurations, and / or functionalities suitable for performing process 100. Additionally, process 100 can include other steps not expressly disclosed herein and / or can include performing the disclosed steps in any order, multiple times, concurrently, and / or sequentially for one tile 25, one uncompressed image 16 A, multiple tiles 25, and / or multiple uncompressed images 16A. Moreover, not all steps of process 100 must be performed, and process 100 can be performed partially and / or entirely in a digital environment by and / or within the systems / components set out in this disclosure, such as system 10 and / or other hardware and software. One, multiple, and / or all steps of process 100 can be performed by one or multiple computer processors, such as processor 20, and / or other hardware and / or software not expressly disclosed herein.

[0072] Process 100 and / or one, multiple, or all steps 102-122 can be configured to manually be performed and / or initiated by an operator / user and / or can be configured to automatically be performed in response to, for example, access to and / or the reception of uncompressed image 16A and / or other information needed to perform that particular step,in response / according to a schedule detailing when that particular step is to be initiated / began and / or completed, and / or any other triggering events / instructions.

[0073] Process 100 can include step 102, which is to access, receive, and / or otherwise obtain uncompressed image 16A of sample 50. The uncompressed image 16A can be captured using any machine / system, such as imaging machine 12. Further, as described above with regards to system 10, uncompressed image 16A can have any format, configuration, size, etc. as is desired and / or necessary to allow for viewing and / or diagnostics of sample 50 as captured in uncompressed image 16 A. In step 102, uncompressed image 16A can be accessed and / or received via any communication methods, such a wired communication, wireless communication, and / or accessed over a centralized and / or distributed server system / network. In one example, imaging machine 12 is on the same network as the system (e.g., system 10) performing process 100 such that the system has access to uncompressed image 16A upon capture by imaging machine 12. Step 102 can include saving uncompressed image to, for example, storage media 22 and / or another digital storage location.

[0074] After accessing and / or receiving uncompressed image 16 A, process 100 can include step 104, which is dividing uncompressed image 16A into multiple uncompressed tiles 25. Step 104 can be performed by processor 20, parse module 26, and / or any other component / system. As described above with regards to parse module 26 of system 10, step 104 can include dividing uncompressed image 16A into any number, digital / electronic size, physical size, shape, organization, configuration, etc. of tiles 25. Step 104 can include saving and / or otherwise storing one, multiple, or all tiles 25 in storage media, such as storage media 22 of system 10. The configurations and functionalities of step 104 are described with regards to parse module 26 above so refer to that discussion for more information regarding step 104.

[0075] Process 100 can include step 106, which is to record a location of one, multiple, or all tiles 25 with respect to uncompressed image 16A. The recordation of the location of each tile 25 within uncompressed image 16A may be useful and / or necessary for reassembling compressed image 16B after compression and / or if tiles 25 / 35 are saved / stored separately. Step 106 can be performed at the same time and / or after the performance of step 104 and by, for example, parse module 26 and / or any of the other components of system 10. The location information of each tile 25 with respect to uncompressed image 16A can be saved at any location, including to storage media 22 and / or associated with the corresponding metadata for each tile 25 / 35.After step 104, process 100 can include step 108, which is to determine the tile FOM value for each tile 25. Step 108 can be performed by processor 20, FOM module 28, and / or any other component / system. As described above with regards to FOM module 28, the tile FOM value for each tile 25 of uncompressed image 16A as determined in step 108 is dependent upon the pixel intensities of the pixels that form each tile and / or the relationship between pixel intensities of the pixels in each tile 25. The tile FOM value can be representative / used to determine whether at least a portion of the tile 25 shows at least a portion of the sample 50. Further, step 108 can include, for use in determining each tile FOM value for each tile 25, determining the pixel intensities of one, multiple, or all pixels that form tile 25 and / or of uncompressed image 16A. Step 108 can include determining each tile FOM value using, for example, the following equation:

[0076]

[0077] " with each tile 25 including a width along an arbitrary x-axis and a height along the corresponding y-axis such that p is the pixel intensity for each pixel px,ythat form the tile 25. After the determination of the tile FOM values for one, multiple, or all tiles 25, step 108 can include saving the tile FOM values to storage media and / or associating the tile FOM value, for example, with the metadata of the corresponding tile 25. The configurations and functionalities of step 108 are described with regards to FOM module 28 and system 10 above so refer to that discussion for more information regarding step 108.

[0078] Step 110 of process 100 can include comparing the tile FOM value for one, multiple, or all tiles 25 to the first FOM threshold value and / or the second FOM threshold value. Step 110 can be performed by processor 20, comparison module 30, and / or any other component / system. Step 110 can also include determining / setting the first FOM threshold value and / or the second FOM threshold value for each tile 25, for each uncompressed image 16A, and / or for all uncompressed images 16A. As described above with regards to comparison module 30, the first FOM threshold value and the second FOM threshold value can be set at any value with the first FOM threshold value representative of a FOM value in which the corresponding tile is certain to have at least a portion of the tile showing at least a portion of sample 50 and the second FOM threshold value representative of a FOM value in which the corresponding tile is certain to show none of sample 50. The comparison in step 110 can result in each tile 25 being categorized as falling within one of threecategories of compression quality: 1) a first, high compression quality corresponding to a tile FOM value that is equal to or greater than the first FOM threshold value; 2) a second, low compression quality corresponding to a tile FOM value that is less than or equal to the second FOM threshold value; and 3) an intermediate compression quality corresponding to a tile FOM value that is between the first FOM threshold value and the second FOM threshold value. Alternatively and / or additionally, step 110 can include comparing the tile FOM value only to the first FOM threshold value and, in steps 112 and / or 114, compressing the tiles 25 only at two different qualities for tile FOM values above the first FOM threshold value and tile FOM values below the first FOM threshold value, respectively. Step 110 can also include providing instructions to, for example, compression module 32 regarding the quality of the compression to use on each tile 25 in steps 112, 114, and / or 116. Step 110 can also provide for the use of user interface 24 to modify the values of the first FOM threshold value and / or the second FOM threshold value depending on the certainty / uncertainty the user desires with regards to tile 25 potentially showing a portion of sample 50. Step 110 can include saving the results of the comparison for each tile 25 to storage media and / or associating the results / determinations, for example, with the metadata of the corresponding tile 25. The configurations and functionalities of step 110 are described with regards to comparison module 30 and system 10 above so refer to that discussion for more information regarding step 110.

[0079] Next, process 100 can include compressing one, multiple, or all tiles 25 of uncompressed image 16A dependent upon the comparison performed in step 110. The compression of each tile 25 can be performed in one of steps 112, 114, and 116 by, for example, compression module 32. Step 112 includes compressing the respective tiles 25 using the first, high compression quality in response to the tile FOM value being equal to or greater than the first FOM threshold value. Step 114 includes compressing the respective tiles 25 using the second, low compression quality in response to the tile FOM value being less than or equal to the second FOM threshold value. Step 116 includes compressing the respective tiles 25 using one of a plurality of intermediate compression qualities in response to the corresponding tile FOM value being between the first FOM threshold value and the second FOM threshold value. As described above with regards to compression module 32 and graphs 70A and 70B, the intermediate compression quality at which each tile 25 is compressed in response to the corresponding tile FOM value being between the first FOM threshold value and the second FOM threshold value can be, for example, determined by referencing a curve having any shape and / or configuration. In one example, step 116includes compressing the tile 25 dependent upon a monotonic curve that is linear (e.g., graph 70A). In another example, step 116 includes compressing the tile 25 dependent upon the following equation:

[0080] >

[0081]

[0082] with qmax being the first compression quality for a tile FOM value that is equal to or greater than the first FOM threshold value, qminbeing the second compression quality for a tile FOM value that is less than or equal to the second FOM threshold value, tsampie being the first FOM threshold value, tciear being the second FOM threshold value, and s being the particular tile FOM value for which the compression quality is being determined.

[0083] As described above with regards to compression module 32 of system 10, the compression quality at which each tile 25 is compressed in steps 112, 114, and 116 reflects the high certainty that the tile 25 shows sample 50 (for the first compression quality), the high certainty that the tile 25 does not show sample 50 (for the second compression quality), and a lower certainty (i.e., uncertainty) that the tile 25 shows sample 50 (for the intermediate compression qualities). In some examples / processors, steps 112, 114, and 116 may only be performed once for each tile 25 depending on the respective tile FOM value of each tile 25 such that each tile 25 is only compressed once. The compression of tiles 25 in steps 112, 114, and 116 results in the modification of tiles 25 and uncompressed image 16A into compressed tiles 35 and compressed image 16B. Compressed image 16B has a reduction in digital storage size compared to uncompressed image 16A and, due to the variable compression of compressed tiles 35 depending upon the certainty that the tiles 35 show sample 50, compressed image 16B has a sufficiently increased visual clarity of sample 50 to allow for viewing and / or diagnostics using sample 50. Steps 112, 114, and / or 116 can be performed concurrently for all tiles 25 within one uncompressed image 16A and / or in any other fashion to compress uncompressed image 16 A.

[0084] Alternatively and / or additionally, steps 112, 114, and / or 116 can include compressing tiles 25 using other compression qualities depending on, for example, the desired diagnostics of sample 50. For example, sample 50 can be at least partially formed of cancerous tissue. In such a situation, the edges of sample 50 may be of increased importance to determine whether sample 50, which was removed during an operation, encompasses the entirety of the cancerous tissue or whether the cancerous tissue extends to the edges of sample 50 (which may imply that the entirety of the cancerous tissue was notremoved during the operation). Thus, in this example, the compression qualities of tiles 25 of uncompressed image 16A may be modified to ensure that tiles 25 that are near the edges and / or show the edges of sample 50 are compressed using a higher compression quality. In this example, graphs 70A and / or 70B may show a curve between the first FOM threshold value and the second FOM threshold value that is not monotonic, and instead the compression quality increases in this area. Other examples of process 100 can include compressing tiles 25 at other compression qualities. The other configurations and functionalities of steps 112, 114, and 116 are described with regards to compression module 32, graphs 70A and 70B, and system 10 above so refer to that discussion for more information regarding steps 112, 114, and 116.

[0085] Process 100 can further include step 118, which is transferring compressed tiles 35 and / or compressed image 16B to, for example, storage server 13 via network connection 34. Step 118 can include transferring and / or otherwise communicating compressed tiles 35 / image 16B via any other method of communication, such as wired and / or wireless communication. In one example, step 118 is performed by making available compressed tiles 35 and / or compressed image 16B via a centralized and / or distributed server system. In other configurations of process 100 and / or system 10, step 118 may not be performed and / or necessary. Other configurations and functionalities of step 118 are described with regards to network connection 34 and / or storage server 13 above so refer to that discussion for more information regarding step 118.

[0086] Similarly, process 100 can further include step 120, which is saving compressed image 16B made up of compressed tiles 35 to storage media 22 in system 10, storage media 14 in storage server 13, and / or to another location. Step 120 can be performed by any component, such as processor 20 and / or any other components of system 10, storage server 13, and / or imaging machine 12. Step 120 can include storing compressed image 16B such that compressed image 16B is configured / organized similarly to uncompressed image 16A except that compressed image 16B is compressed. Thus, step 120 can include referencing the location of each compressed tile 35 as recorded in step 106. Compressed image 16B can be saved in its entirety, and / or compressed tiles 35 can be saved individually. Step 120 can be performed before, concurrent with, and / or after step 118. Other configurations and functionalities of step 120 are described with regards to storage media 14 and / or 22 above so refer to that discussion for more information regarding step 120.Finally, process 100 can include step 122, which is displaying compressed image 16B via user interface 24 and / or via other methods. The display of compressed image 16B in step 122 can be useful, for example, for viewing and / or diagnostic purposes regarding sample 50 shown in compressed image 16B. Because process 100 compresses tiles 35 of image 16B at different qualities depending upon whether the tile 35 shows a portion of sample 50 (i.e., depending on the certainty that the tile 35 shows sample 50), compressed image 16B shows sample 50 at an increased visual clarity as compared to other images that were compressed using the same compression quality across the entirety of the image. The increased clarity allows for a clearer image and thus allows for improved viewability of sample 50 in compressed image 16B. Step 122 can be performed at any time throughout process 100, and / or other configurations of process 100 can include not performing step 122 and / or performing step 122 multiple times. Other configurations and functionalities of step 122 are described with regards to user interface 24 and system 10 generally so refer to that discussion for more information regarding step 122.

[0087] Process 100 can include other steps not expressly disclosed herein and / or not shown in FIG. 2, and / or process 100 can omit steps shown in FIG. 2. For example, process 100 can include capturing uncompressed image 16A of sample 50 by imagining machine 12 and / or by another imaging system. Further, process 100 can be performed dozens or hundreds of times regarding images showing any type of sample and / or object.

[0088] The following are examples and / or embodiments of the present disclosure: A first example method of reducing a digital storage size of an image of a sample for use in digital pathology operations is disclosed herein that includes receiving the image of the sample in a digital format; automatically dividing, by a computer processor, the image into multiple tiles that collectively form the image; and determining, by the computer processor and for each tile of the multiple tiles, a tile figure of merit (FOM) value depending on pixel intensities of pixels that form each tile. The method can further include comparing the tile FOM value for each tile to a first FOM threshold value and a second FOM value and: 1) in response to the tile FOM value being equal to or greater than to the first FOM threshold value, determining with high certainty that the tile shows at least a portion of the sample and digitally compressing the corresponding tile using a first compression quality; 2) in response to the tile FOM value being less than or equal to the second FOM threshold value, determining with high certainty that the tile does not show at least a portion of the sample and digitally compressing the corresponding tile using a second compression quality that is lower than the first compression quality; and 3) inresponse to the tile FOM value being between the first FOM threshold value and the second FOM threshold value, digitally compressing the corresponding tile at one of a plurality of intermediate compression qualities between the first compression quality and the second compression quality depending on the tile FOM value.

[0089] Any of the examples / embodiments disclosed herein can optionally include, additionally and / or alternatively, any one or more of the preceding and / or subsequent examples, embodiments, elements, features, configurations, steps, and / or components.

[0090] The examples disclosed herein can include that the compression quality for each tile with the FOM value being between the first FOM threshold value and the second FOM threshold value is determined by a curve that has a value equal to the first compression quality at the first FOM threshold value and the second compression quality at the second FOM threshold value.

[0091] The examples disclosed herein can include that the curve is linear from the first FOM threshold value to the second FOM threshold value.

[0092] The examples disclosed herein can include that the curve is monotonic and decreases at a greater rate regarding the compression quality versus the tile FOM value as the tile FOM value is farther from the first FOM threshold value.

[0093] The examples disclosed herein can include that the digital format of the image is at least one of Joint Photographic Experts Group (JPEG) format and Digital Imaging and Communications in Medicine (DICOM) format.

[0094] The examples disclosed herein can include transmitting a compressed image made up of the compressed tiles via at least one of a network connection and Digital Imaging and Communications in Medicine (DICOM) communication protocols.

[0095] The examples disclosed herein can include that the step of dividing the image into multiple tiles further comprises dividing the image into multiple tiles with each tile having a width of 256 pixels and a height of 256 pixels.

[0096] The examples disclosed herein can include that a tile with a tile FOM value that is less than the first FOM threshold value but greater than the second FOM threshold value and is closer to the first FOM threshold value than the second FOM threshold value is more certain to show at least a portion of the sample as compared to a tile with a tile FOM value that is closer to the second FOM threshold value.

[0097] The examples disclosed herein can include that the multiple tiles include at least one hundred tiles that form the image and the computer processor concurrently determines the tile FOM value for each tile, concurrently compares each tile FOM value tothe first FOM threshold value and the second FOM threshold value for each tile, and concurrently compresses each tile depending on the corresponding tile FOM value.

[0098] The examples disclosed herein can include saving a compressed image made up of the compressed tiles to storage media.

[0099] The examples disclosed herein can include that the compression quality of each tile having the tile FOM value that is between the first FOM threshold value and the second FOM threshold value is determined using the following equation:

[0100] <

[0101]

[0102] and qmax is the first compression quality for a tile FOM value that is equal to or greater than the first FOM threshold value, qmm is the second compression quality for a tile FOM value that is less than or equal to the second FOM threshold value, tsampie is the first FOM threshold value, tciearis the second FOM threshold value, and .y is the tile FOM value.

[0103] A second example method of reducing a digital storage size of an image of a sample for use in digital pathology operations is disclosed herein that includes obtaining the image in a digital format; automatically digitally dividing, by a computer processor, the image into multiple tiles that collectively form the image; and determining, by the computer processor and for each tile of the multiple tiles, a tile figure of merit (FOM) value depending on pixel intensities of pixels that form each tile. The method can further include automatically comparing the tile FOM value for each tile to a FOM threshold value with a tile FOM value that is above the FOM threshold being determined with high certainty to show at least a portion of the sample and a tile FOM value that is below the FOM threshold being determined with less certainty to show at least a portion of the sample; compressing, in response to the tile FOM value for the corresponding tile being equal to or greater than the FOM threshold value, the tile using a first compression quality; and compressing, in response to the tile FOM value for the corresponding tile being less than the FOM threshold value, the tile using a second compression quality that is lower than the first compression quality.

[0104] Any of the examples / embodiments disclosed herein can optionally include, additionally and / or alternatively, any one or more of the preceding and / or subsequent examples, embodiments, elements, features, configurations, steps, and / or components.

[0105] The examples disclosed herein can include that the multiple tiles include at least one hundred tiles and the step of digitally dividing the image into multiple tiles furthercomprises dividing the image into the multiple tiles with each tile having a width of at least 250 pixels and a height of at least 250 pixels.

[0106] The examples disclosed herein can include that the step of digitally dividing the image into multiple tiles further comprises recording a location of each tile with respect to the location of the tile to the overall image.

[0107] An example system of reducing a digital storage size of an image of a sample for use in digital pathology operations is disclosed herein that includes the image of the sample in a digital format, a parse module that functions at least partially in conjunction with a computer processor and is configured to automatically divide the image into multiple tiles that collectively form the image, a figure of merit (FOM) module that functions at least partially in conjunction with the computer processor and is configured to determine a tile FOM value for each tile depending on pixel intensities of pixels that form each tile, and a comparison module that functions at least partially in conjunction with the computer processor and is configured to automatically compare each tile FOM value to a first FOM threshold value and provide instructions to a compression module depending upon the comparison. The system can also include the compression module that functions at least partially in conjunction with the computer processor and is configured to, in response to the tile FOM value being equal to or greater than the first FOM threshold value, digitally compressing the corresponding tile using a first compression quality due to the comparison module determining with high certainty that the tile shows at least a portion of the sample and, in response to the tile FOM value being less than the first FOM threshold, digitally compressing the corresponding tile using a second compression quality that is lower than the first compression quality.

[0108] Any of the examples / embodiments disclosed herein can optionally include, additionally and / or alternatively, any one or more of the preceding and / or subsequent examples, embodiments, elements, features, configurations, steps, and / or components.

[0109] The examples disclosed herein can include that the comparison module is configured to automatically compare each tile FOM value to a second threshold value that is less than the first threshold value and provide instruction to the compression module depending on the comparison to the second threshold value.

[0110] The examples disclosed herein can include that the second compression quality includes a lowest-quality compression quality and a plurality of intermediate compression qualities that is between the first compression quality and the lowest-quality compression quality.The examples disclosed herein can include that the compression module is configured to in response to the tile FOM value being less than or equal to the second FOM threshold value, digitally compress the corresponding tile using the lowest-quality compression quality due to the comparison module determining with high certainty that the tile does not show at least a portion of the sample and, in response to the tile FOM value being between the first FOM threshold value and the second FOM threshold value, digitally compress the corresponding tile at one of the plurality of intermediate compression qualities between the first compression quality and the lowest-quality compression quality depending on the tile FOM value.

[0111] The examples disclosed herein can include storage media to which the multiple tiles that form the image are digitally saved via transmission over a network connection.

[0112] The examples disclosed herein can include that each tile of the multiple tiles includes a width along an x-axis and a height along a y-axis and the tile FOM value is determined using the following equation:

[0113]

[0114] and p is the pixel intensity for each pixel of the corresponding tile.

[0115] The examples disclosed herein can include that the image is divided into at least a hundred tiles by the parse module and a location of each tile with respect to the image is recorded.

[0116] The examples disclosed herein can include a user interface configured to display a compressed image made up of the compressed tiles.

[0117] While the invention has been described with reference to an exemplary embodiment(s), it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment(s) disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims

CLAIMS:

1. A method of reducing a digital storage size of an image of a sample for use in digital pathology operations, the method comprising:receiving the image of the sample in a digital format;automatically dividing, by a computer processor, the image into multiple tiles that collectively form the image;determining, by the computer processor and for each tile of the multiple tiles, a tile figure of merit (FOM) value depending on pixel intensities of pixels that form each tile;comparing the tile FOM value for each tile to a first FOM threshold value and a second FOM value and:in response to the tile FOM value being equal to or greater than to the first FOM threshold value, determining with high certainty that the tile shows at least a portion of the sample and digitally compressing the corresponding tile using a first compression quality;in response to the tile FOM value being less than or equal to the second FOM threshold value, determining with high certainty that the tile does not show at least a portion of the sample and digitally compressing the corresponding tile using a second compression quality that is lower than the first compression quality; and in response to the tile FOM value being between the first FOM threshold value and the second FOM threshold value, digitally compressing the corresponding tile at one of a plurality of intermediate compression qualities between the first compression quality and the second compression quality depending on the tile FOM value.

2. The method of claim 1 , wherein the compression quality for each tile with the FOM value being between the first FOM threshold value and the second FOM threshold value is determined by a curve that has a value equal to the first compression quality at the first FOM threshold value and the second compression quality at the second FOM threshold value.

3. The method of claim 2, wherein the curve is linear from the first FOM threshold value to the second FOM threshold value.

4. The method of claim 2 wherein the curve is monotonic and decreases at a greater rate regarding the compression quality versus the tile FOM value as the tile FOM value is farther from the first FOM threshold value.

5. The method of claim 1, wherein the digital format of the image is at least one of Joint Photographic Experts Group (JPEG) format and Digital Imaging and Communications in Medicine (DICOM) format.

6. The method of claim 1 , further comprising:transmitting a compressed image made up of the compressed tiles via at least one of a network connection and Digital Imaging and Communications in Medicine (DICOM) communication protocols.

7. The method of claim 1, wherein the step of dividing the image into multiple tiles further comprises:dividing the image into multiple tiles with each tile having a width of 256 pixels and a height of 256 pixels.

8. The method of claim 1, wherein a tile with a tile FOM value that is less than the first FOM threshold value but greater than the second FOM threshold value and is closer to the first FOM threshold value than the second FOM threshold value is more certain to show at least a portion of the sample as compared to a tile with a tile FOM value that is closer to the second FOM threshold value.

9. The method of claim 1, wherein the multiple tiles include at least one hundred tiles that form the image and the computer processor concurrently determines the tile FOM value for each tile, concurrently compares each tile FOM value to the first FOM threshold value and the second FOM threshold value for each tile, and concurrently compresses each tile depending on the corresponding tile FOM value.

10. The method of claim 1 , further comprising:saving a compressed image made up of the compressed tiles to storage media.

11. The method of claim 1 , wherein the compression quality of each tile having the tile FOM value that is between the first FOM threshold value and the second FOM threshold value is determined using the following equation:<and qmax is the first compression quality for a tile FOM value that is equal to or greater than the first FOM threshold value, qminis the second compression quality for a tile FOMvalue that is less than or equal to the second FOM threshold value, tsampie is the first FOM threshold value, tciear is the second FOM threshold value, and 5 is the tile FOM value.

12. A method of reducing a digital storage size of an image of a sample for use in digital pathology operations, the method comprising:obtaining the image in a digital format;automatically digitally dividing, by a computer processor, the image into multiple tiles that collectively form the image;determining, by the computer processor and for each tile of the multiple tiles, a tile figure of merit (FOM) value depending on pixel intensities of pixels that form each tile;automatically comparing the tile FOM value for each tile to a FOM threshold value with a tile FOM value that is above the FOM threshold being determined with high certainty to show at least a portion of the sample and a tile FOM value that is below the FOM threshold being determined with less certainty to show at least a portion of the sample;compressing, in response to the tile FOM value for the corresponding tile being equal to or greater than the FOM threshold value, the tile using a first compression quality; andcompressing, in response to the tile FOM value for the corresponding tile being less than the FOM threshold value, the tile using a second compression quality that is lower than the first compression quality.

13. The method of claim 12, wherein the multiple tiles include at least one hundred tiles and the step of digitally dividing the image into multiple tiles further comprises:dividing the image into the multiple tiles with each tile having a width of at least 250 pixels and a height of at least 250 pixels.

14. The method of claim 12, wherein the step of digitally dividing the image into multiple tiles further comprises:recording a location of each tile with respect to the location of the tile to the overall image.

15. A system of reducing a digital storage size of an image of a sample for use in digital pathology operations, the system comprising:the image of the sample in a digital format;a parse module that functions at least partially in conjunction with a computer processor and is configured to automatically divide the image into multiple tiles that collectively form the image;a figure of merit (FOM) module that functions at least partially in conjunction with the computer processor and is configured to determine a tile FOM value for each tile depending on pixel intensities of pixels that form each tile; a comparison module that functions at least partially in conjunction with the computer processor and is configured to automatically compare each tile FOM value to a first FOM threshold value and provide instructions to a compression module depending upon the comparison;the compression module that functions at least partially in conjunction with the computer processor and is configured to:in response to the tile FOM value being equal to or greater than the first FOM threshold value, digitally compressing the corresponding tile using a first compression quality due to the comparison module determining with high certainty that the tile shows at least a portion of the sample; andin response to the tile FOM value being less than the first FOM threshold, digitally compressing the corresponding tile using a second compression quality that is lower than the first compression quality.

16. The system of claim 15, wherein:the comparison module is configured to automatically compare each tile FOM value to a second threshold value that is less than the first threshold value and provide instruction to the compression module depending on the comparison to the second threshold value;the second compression quality includes a lowest-quality compression quality and a plurality of intermediate compression qualities that is between the first compression quality and the lowest-quality compression quality; and the compression module is configured to:in response to the tile FOM value being less than or equal to the second FOM threshold value, digitally compress the corresponding tile using the lowest-quality compression quality due to the comparison module determining with high certainty that the tile does not show at least a portion of the sample; andin response to the tile FOM value being between the first FOM threshold value and the second FOM threshold value, digitally compress the corresponding tile at one of the plurality of intermediate compression qualities between the first compression quality and the lowest-quality compression quality depending on the tile FOM value.

17. The system of claim 15, further comprising:storage media to which the multiple tiles that form the image are digitally saved via transmission over a network connection.

18. The system of claim 15, wherein each tile of the multiple tiles includes a width along an x-axis and a height along a y-axis and the tile FOM value is determined using the following equation:and p is the pixel intensity for each pixel of the corresponding tile.

19. The system of claim 15, wherein the image is divided into at least a hundred tiles by the parse module and a location of each tile with respect to the image is recorded.

20. The system of claim 15, further comprising:a user interface configured to display a compressed image made up of the compressed tiles.

Images