Image processing method and apparatus for reducing noise contained in an image acquired by radiography

Abstract

an image processing device for performing noise reduction processing on an image obtained by radiation imaging using a recursive method, the image processing device including: an image input unit for receiving an input of an image for each frame obtained by radiation imaging; a motion map generating unit for generating a motion map including motion detection information for each pixel of a difference map obtained by subtracting a current frame image from a previous frame image; a cumulative motion map generating unit for generating a cumulative motion map based on the generated motion map and a motion map accumulated up to a previous frame; a statistical value map generating unit for generating a statistical value map formed by assigning a statistical value indicating a motion probability of each pixel to each pixel of the cumulative motion map; and an output image generating unit for generating an output image of the current frame by mixing the current frame image and an output image of the previous frame based on the cumulative motion map.

Description

[Technical field]

[0001] The present invention relates to an image processing method and apparatus for reducing noise contained in an image acquired by radiography. [Background technology]

[0002] Various medical diagnoses and treatments are performed based on images captured by radiography devices that use radiation such as X-rays. In general, medical images captured using X-rays contain not only anatomical information of the subject, i.e., the human body, but also noise generated due to the imaging environment and equipment performance. Such noise deteriorates the image and reduces the ability to read the patient's anatomy. The degree of image deterioration is determined by various factors, such as image deterioration due to noise in the sensor itself and image deterioration due to noise generated by the amount of X-rays.

[0003] Meanwhile, prolonged exposure to radiation generated during X-ray imaging can cause side effects due to radiation exposure. For this reason, it is required to take X-ray images by irradiating low-dose X-rays in order to minimize radiation exposure. However, when irradiating low-dose X-rays, the photon density of the incident X-rays decreases, which significantly increases the concentration of photon mottle, resulting in a problem of reduced image quality.

[0004] For this reason, various methods have been introduced to effectively remove noise from acquired images while using low-dose X-rays. For example, Korean Patent No. 10-1432864 introduces a noise reduction technology using recursive filtering, in which a noise component image is obtained through threshold processing and reduction processing on a difference map obtained by subtracting the image of the current frame from the image of the previous frame, and this is added to the image of the current frame to generate an output image of the current frame, and the noise-reduced output image is stored in memory.

[0005] However, the noise reduction technique using the recursive filtering method is very effective in removing line noise and applying it to a fixed object without motion, but the larger the motion of the object, the lower the noise reduction performance. In particular, if the motion of the object is erroneously determined, the quality of the image is degraded due to motion blur. Therefore, in the recursive filtering method, it is very important to set a threshold value for discriminating between object motion and noise in a difference map between a current frame and a previous frame. In the Korean Patent Registration No. 10-1432864, if the absolute value of each pixel of the difference map exceeds a preset threshold value that is larger than the standard deviation of the noise, it is determined that there is object motion, and if not, it is determined that there is no object motion. However, due to the characteristics of X-ray images, the noise intensity and deviation vary greatly depending on the dose conditions, the density of the object, and the degree of the object motion during shooting, so the range of the threshold value becomes large, making it difficult to set an appropriate threshold value. In addition, if there is object motion, the effect of recursive filtering is suppressed and the noise reduction effect is also reduced. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Korean Patent No. 10-1432864 [Patent Document 2] Patent No. 6744440 Summary of the Invention [Problem to be solved by the invention]

[0007] The problem to be solved by the present invention is to provide a method for effectively reducing noise by using a cumulative motion map in which images that have been subjected to noise reduction processing based on the motion comparison results in a difference map are accumulated in chronological order. [Means for solving the problem]

[0008] An image processing device according to an embodiment of the present invention performs noise reduction processing on an image obtained by radiation imaging using a recursive method. The image processing device includes an image input unit for receiving an image for each frame obtained by radiation imaging, a motion map generating unit for generating a motion map including motion detection information for each pixel of a difference map obtained by a difference between a current frame image and a previous frame image, a cumulative motion map generating unit for generating a cumulative motion map based on the generated motion map and a motion map accumulated up to a previous frame, a statistical value map generating unit for generating a statistical value map formed by assigning a statistical value indicating a motion probability of each pixel to each pixel of the cumulative motion map, and an output image generating unit for generating an output image of the current frame by mixing the current frame image and an output image of the previous frame based on the cumulative motion map.

[0009] The motion map generation unit may be configured to utilize the statistics map to generate the motion map.

[0010] The motion map generation unit may include a difference map generation unit that generates the difference map based on a difference between the current frame image and the previous frame image, and a threshold processing unit that generates the motion map including the motion detection information of each pixel by thresholding pixel values ​​of each pixel of the difference map.

[0011] The thresholding unit may be configured to threshold the difference map through an adaptive threshold whose magnitude depends on the magnitude of a statistic of the statistical value map.

[0012] The thresholding unit may be configured to set a lower threshold for each pixel for the thresholding process the greater the motion probability indicated by the statistics of the corresponding pixel in the statistics map.

[0013] The statistical value map generating unit may be configured to calculate the statistical value in units of a pixel mask including a plurality of pixels including each pixel of the cumulative motion map, and assign the statistical value as a statistical value for each pixel to generate the statistical value map.

[0014] The statistic may be the entropy, variance or standard deviation of pixel values ​​of a plurality of pixels of the pixel mask.

[0015] The output image generation unit may be configured to determine a blending ratio between the current frame image and the previous frame output image based on a cumulative motion map updated using the statistical value map.

[0016] The cumulative motion map generation unit may be configured to threshold the statistical map and to update the cumulative motion map by selectively increasing or decreasing pixel values ​​of pixels of the cumulative motion map using the thresholded statistical map.

[0017] The difference map may be obtained using one or more images of the current frame image and a previous frame.

[0018] The previous frame image may be an output image of the previous frame.

[0019] Meanwhile, the cumulative motion map generating unit may be configured to update the cumulative motion map using the statistics map.

[0020] According to an embodiment of the present invention, the output image generation unit may be configured to determine a ratio of blending the current frame image and the previous frame output image based on a cumulative motion map updated using the statistical value map.

[0021] An image processing device according to an embodiment of the present invention, which performs noise reduction processing on an image obtained by radiography using a recursive method, includes the steps of receiving an input of an image for each frame obtained by radiography, generating a motion map including motion detection information for each pixel of a difference map obtained by subtracting a current frame image from a previous frame image, generating a cumulative motion map based on the generated motion map and a motion map accumulated up to the previous frame, generating a statistical value map by assigning a statistical value indicating a motion probability of a plurality of pixels including each pixel to each pixel of the cumulative motion map, and generating an output image of the current frame by mixing the current frame image and an output image of the previous frame based on the cumulative motion map.

[0022] In the generating of the motion map, the motion map may be generated using the statistical value map.

[0023] In the motion map generating step, the motion map including the motion sensing information of each pixel may be generated through thresholding of pixel values ​​of each pixel of the difference map, and the thresholding may be configured to be performed through an adaptive threshold whose magnitude varies depending on the magnitude of the statistical value of the statistical value map.

[0024] In the motion map generation step, the motion map including the motion sensing information of each pixel may be generated through thresholding of pixel values ​​of each pixel of the difference map, and the threshold value of each pixel for the thresholding may be set lower as the motion probability indicated by the statistical value of the corresponding pixel of the statistical value map increases.

[0025] In the generating of the statistical map, the statistical map may be generated by calculating the statistical values ​​in units of a pixel mask including a plurality of pixels including each pixel of the cumulative motion map, and allocating the statistical values ​​as the statistical values ​​of each pixel.

[0026] The statistic may be the entropy, variance or standard deviation of pixel values ​​of a plurality of pixels of the pixel mask.

[0027] The step of generating the output image may include the steps of updating the cumulative motion map using the statistical value map, and determining a ratio of mixing the current frame image and the previous frame output image based on the updated cumulative motion map.

[0028] The step of updating the cumulative motion map may be configured to perform a threshold process on the statistical value map, and to selectively increase or decrease pixel values ​​of pixels of the cumulative motion map using the thresholded statistical value map to update the cumulative motion map.

[0029] The difference map may be obtained by subtracting the current frame image from the output image of a previous frame.

[0030] Meanwhile, the image processing method according to another embodiment of the present invention may further include updating the cumulative motion map by using the statistical value map.

[0031] According to an embodiment of the present invention, in generating the output image, a blending ratio of the current frame image and the previous frame output image may be determined based on the updated cumulative motion map. Effect of the Invention

[0032] According to the present invention, an image with reduced noise can be generated through a cumulative motion map. [Brief description of the drawings]

[0033] [Figure 1] 1 is a diagram illustrating an example of a C-arm type radiation imaging apparatus to which an image processing device according to an embodiment of the present invention is applied; [Diagram 2]1 is a block diagram of a video processing device according to an embodiment of the present invention; [Diagram 3] 2 is a flowchart of an image processing method according to an embodiment of the present invention. [Figure 4] 1 is a graph showing a comparison between the distribution of pixel values ​​of a difference map of an original signal and the distribution of pixel values ​​of a difference map obtained after the Anscombe transform for the same input video (previous frame and current frame) of a stationary subject. [Diagram 5] 2 is a diagram illustrating an algorithm of an image processing method according to an embodiment of the present invention; [Figure 6] An example of a motion map output when the hand phantom is moved from right to left in the previous frame image and the current frame image is shown. [Figure 7] 13 is a diagram illustrating a method of generating a cumulative motion map using a motion map. [Figure 8] 13 is a diagram showing generation of a motion map and an accumulated motion map according to an input image with frame progression; [Figure 9] Examples of motion maps and output images obtained when a thin lead rod passes over a stationary pelvis phantom are shown. [Figure 10] 9. An example of a motion map is shown for cases where the threshold for motion detection is increased and decreased under the same conditions as in FIG. [Figure 11] 1 shows an example of an entropy map generated by calculating entropy in a masking operation of pixels in a cumulative motion map. [Figure 12] 11 is a diagram illustrating a method of generating a motion map according to another embodiment of the present invention. [Figure 13] 11 is a diagram illustrating an algorithm of an image processing method according to another embodiment of the present invention; [Figure 14] 14 is a diagram illustrating a process of updating a cumulative motion map using a thresholded entropy map in the image processing method of FIG. 13; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0034] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in detail with reference to the accompanying drawings, in which: FIG. 1 is a block diagram of a semiconductor device according to an embodiment of the present invention;

[0035] An example of a C-arm type radiation imaging apparatus to which an image processing device having a noise reduction function according to an embodiment of the present invention is applied is shown in Fig. 1. The image processing device according to the embodiment of the present invention may be applied to a part of the image imaging apparatus as shown in Fig. 1, or may be configured as a separate device from the image imaging apparatus to perform an image processing function.

[0036] An image capturing device to which an image processing device according to an embodiment of the present invention is applied may be configured as an image capturing device capable of acquiring moving images, for example, configured in the form of a C-arm as shown in FIG. 1, and may be configured to capture an area of ​​interest of a subject S to be captured using radiation such as X-rays.

[0037] 1, the radiography apparatus may include a radiation irradiator 110 that outputs radiation, for example, X-rays, and an image acquirer 120 that detects radiation transmitted through a subject S to acquire image data, and the radiation irradiator 110 and the image acquirer 120 may be supported on both ends of a C-arm 210. For example, the radiography apparatus may be applied to a mobile C-arm X-ray imaging device, an interventional X-ray device, an interventional angiography C-arm X-ray device, etc.

[0038] The support structure 200 supports the radiation emitting unit 110 and the image acquiring unit 120, and is configured to change the spatial and rotational positions of the radiation emitting unit 110 and the image acquiring unit 120 in order to change the imaging position and angle of the irradiation subject S. For example, the support structure 200 may include a support body 240, a lift column 230 connected to the support body 240 so as to be movable in a vertical direction D1, and a forward / backward moving arm 220 connected to the lift column 230 so as to be movable in a vertical direction D2 relative to the lift column 230.

[0039] The C-arm 210 is fastened to the forward / backward movement arm 220 so as to be rotatable relative to the forward / backward movement arm 220 in at least one rotation direction, and the radiation irradiation unit 110 and the image acquisition unit 120 are fastened to both ends of the C-arm 210. At this time, the C-arm 210 can move up and down together with the forward / backward movement arm 220 and move forward and backward in the horizontal direction, and can be fastened to the forward / backward movement arm 220 so as to be rotatable relative to the forward / backward movement arm 220 in at least one rotation direction, for example, an orbital rotation direction R1 and an axial rotation direction R2 centered on a direction parallel to the horizontal movement direction of the forward / backward movement arm 220. Although not shown in the drawings, the support structure 200 may include an actuator such as a motor for the upward and downward movement of the lift column 230, the horizontal movement of the forward / backward movement arm 220, and the rotation of the C-arm 210. Elements for supporting and driving the C-arm 210, which is a supporting member supporting the radiation irradiation unit 110 and the image acquisition unit 120, i.e., the forward / backward arm 220, the lift column 230, and the actuators provided thereon, can be said to be driving elements for driving the C-arm 210, and a combination of these can be said to be a driving unit for driving the C-arm 210. In addition, the forward / backward arm 220 can be configured to perform panning rotation of the C-arm 210 through lateral rotation. The shape of the supporting member is not limited to a C-shape, and in other embodiments of the present invention, a U-shaped, G-shaped, or other arm can be used as the supporting member instead of the C-shape.

[0040] The display unit 140 is configured to display at least one of real-time position information, image data, reference position information, and radiation output information. The display unit 140 may be any device capable of displaying information and images, such as a printer, a CRT display, an LCD display, a PDP display, an OLED display, an FED display, an LED display, a DLP display, a PFD display, a 3D display, a transparent display, etc. The display unit 140 may also be implemented in a form capable of displaying and inputting information, such as a touch screen that can receive input from a user.

[0041] The image processing device 10 according to the embodiment of the present invention may be embodied in the form of an information processing device such as one or more computers capable of performing information processing and calculation. For example, the computer may include a control means such as a CPU, a storage means such as a read only memory (ROM) or a random access memory (RAM), and a graphic control means such as a graphics processing unit (GPU). The computer may also include a communication means such as a network card, and an input / output means such as a keyboard, a display, or a touch screen. Such computer components may be connected via a bus as is well known, and may be operated and controlled by the execution of a program stored in the storage means.

[0042] The image processing device 10, which may be embodied in the form of a computer capable of information processing, may be configured to be installed in the radiation imaging device shown in FIG. 1 and to perform an image processing function. In this case, it may be configured to receive and process images captured as part of the radiation imaging device, and to display the processed images on an image display means of the radiation imaging device.

[0043] 2, an image processing device 10 according to an embodiment of the present invention includes a number of functional components for noise reduction processing, namely, an image input unit 11, a difference unit 14, a threshold processing unit 15, an accumulated motion map generating unit 16, and an output image generating unit 17. At this time, a motion map is generated by the successive operations of the difference unit 14 and the threshold processing unit 15, and in this sense, the difference unit 14 and the threshold processing unit 15 can be regarded as a motion map generating unit.

[0044] 3, an image processing method according to an embodiment of the present invention that can be performed by the image processing device 10 may include an image acquisition step (S31), a difference map generation step (S34), a motion map generation step (S35), an accumulated motion map generation step (S36), a statistical value map (e.g., an entropy map) generation step (S38), and an image output step (S37). The image processing method generates an output image in which noise is reduced through a recursive method. In this respect, this method is also called infinite recursive depth.

[0045] The image input unit 11 receives an input image from the outside. For example, the image input unit 11 may receive frames of a moving image acquired by the image acquiring unit 120 in a time-sequential manner. An image consisting of a plurality of pixels of an area including an object may be acquired as a radiation moving image sequence by the image acquiring unit 120, which is a radiation detection panel, in a time-sequential manner, and the acquired radiation moving image sequence may be input to the image input unit 11 in a time-sequential manner.

[0046] The input image may be one frame of a video consisting of multiple frames. The input image may also be a two-dimensional image having a plurality of pixels consisting of a plurality of rows (n rows) and a plurality of columns (m columns), and may include noise along with the captured object. For example, the noise included in the input image may include line noise and random noise in a predetermined direction, for example, horizontal and / or vertical direction.

[0047] A motion map is generated through difference map generation by the difference unit 14 and threshold processing of the difference map by the threshold processing unit 15. As a result, a motion map can be generated from the noise-processed current frame image and the previous frame image, for example, the output image of the previous frame, as shown in FIG. 5. Here, the motion map is obtained from data obtained by subtracting the previous frame image from the current frame image, and includes information on the presence or absence of motion for each pixel, for example, pixels with motion can be set to have a value of "0" and pixels without motion can be set to have a value of "1". That is, all pixels in the motion map have a pixel value of 0 or 1, and a pixel with a value of 0 can mean that there is motion with reference to the previous frame image, and a pixel with a value of 1 can mean that there is no motion with reference to the previous frame image. The presence or absence of motion can be determined based on threshold processing, and in an embodiment of the present invention, a statistical value map, for example, an entropy map, is used during threshold processing for motion determination.

[0048] The difference unit 14 generates a difference map by subtracting the current frame image from the previous frame image, for example, the output image of the previous frame. At this time, the difference may be performed after noise processing, for example, noise reduction and noise stabilization processing, is performed on the current frame image and the output image of the previous frame. By using the output image of the previous frame to generate the difference map in the current frame, a noise reduction process of a so-called recursive filtering structure may be performed. The difference unit 14 may generate the difference map by calculating the pixel value difference of each pixel at the same position for all pixels between the current frame image and the previous frame image. That is, the difference map includes the same number of pixels as the frame images, and the value of each pixel in the difference map corresponds to the difference value of the corresponding pixel between the current frame image and the previous frame image. Here, it should be understood that the previous frame image is a concept including not only the image of the frame immediately before the current frame, but also the image of the frame before that. At this time, the difference map may be obtained by subtracting the image of the current frame, for example, the input image, from the image of one or more frames of the previous frames, for example, the output image. For example, the difference map can be generated by subtracting one of the output images of the previous frames from the input image of the current frame, or can be generated by subtracting a mixed image obtained by mixing the output images of multiple previous frames from the input image of the current frame.

[0049] The difference map may include object motion information and residual noise information. Additionally, the difference map may be stabilized by applying a mean filter or a median filter to the generated difference map.

[0050] The threshold processing unit 15 determines a threshold for motion detection of the difference map, detects the presence or absence of motion of each pixel by performing threshold processing based on the determined threshold, and generates a motion map based on the motion. That is, the threshold processing unit 15 determines the presence or absence of motion of each pixel of the difference map through threshold processing, and generates a motion map including information indicating the presence or absence of motion of each pixel based on the determination. If the threshold for motion detection is set too low, the motion detection sensitivity increases and the noise reduction level decreases, and conversely, if the threshold is set too high, the motion detection sensitivity decreases and motion blur (pulling phenomenon) may occur. Since X-ray images are acquired under different dose conditions and subject characteristics, it is difficult to predict pixel values ​​of the acquired images, and it is therefore necessary to set an appropriate threshold according to the pixel values.

[0051] The threshold for motion detection may be determined by various methods such as statistical methods, experimental methods, and arithmetic methods. FIG. 4 shows a comparison between the pixel value distribution of the difference map of the original signal and the pixel value distribution of the difference map obtained after the Anscombe transform for the same input image (previous frame and current frame) of a stationary subject. FIG. 4(a) is a graph showing the pixel value distribution of the difference map obtained from the original signal, and FIG. 4(b) is a graph showing the pixel value distribution of the difference map obtained after the Anscombe transform of the original signal. In the graph of FIG. 4, the horizontal axis (x-axis) indicates pixel value (intensity), and the vertical axis (y-axis) indicates the pixel value difference of the pixel at the same position between the previous frame image and the current frame image. Since the subject is stationary, the y-axis direction data distribution of any x value can be considered as noise, and if the y-axis direction data distribution is equal to or greater than the noise value, it can be estimated as a pixel where motion has occurred.

[0052] For example, in the distribution of the difference map of the original signal, if the pixel value is 500, the appropriate threshold for motion determination is about 100, and if the pixel value is 2200, the appropriate threshold is about 170. As such, the appropriate threshold for motion determination varies greatly depending on the pixel value, making it difficult to determine the threshold. In contrast, in the distribution of the difference map of the Anscombe transformed signal as shown in FIG. 4(b), if the pixel value is 500, the appropriate threshold is about 5, and if the pixel value is 2200, the appropriate threshold is about 4. As such, when the difference map is generated by subtracting the Anscombe transformed signal, the appropriate difference in thresholds according to the pixel value becomes very small, making it very easy to determine the threshold. In this case, for example, the threshold may be set to a value that belongs to the upper 70 to 80% range of the distribution of pixel value differences for a specific pixel value.

[0053] Even if the difference data is calculated from the Anscombe transformed signal, there will still be some deviation when applying the same threshold to all pixel values. Taking this into consideration, adaptive thresholding can be used to more fairly correct the noise deviation for all pixel values. The reason why the difference value changes nonlinearly depending on the pixel value is due to the mathematical characteristics of the Anscombe transform.

[0054] The threshold processing unit 15 generates a motion map by performing threshold processing for motion detection on the difference map generated by the difference unit 14. The threshold processing unit 15 generates a motion map from the difference map by applying an adaptive threshold according to a statistical map, e.g., an entropy map, generated by a statistical map (e.g., entropy map) generation unit 18 described below. A method of determining whether motion is detected using the adaptive threshold and generating a motion map based on the determined result will be described below.

[0055] The threshold processing unit 15 determines that each pixel in the difference map is a pixel with motion if its pixel value is equal to or greater than a predetermined threshold, and assigns a judgment value indicating motion, for example, "0," to the pixel. Conversely, if its pixel value is less than the threshold, the threshold processing unit 15 determines that the pixel is a pixel with no motion, and assigns a judgment value indicating no motion, for example, "1," to the pixel. The threshold processing unit 15 performs such motion judgment using a threshold for all pixels in the difference map, and assigns a judgment value according to the result to the pixel to generate a motion map. In this manner, the threshold processing unit 15 generates a motion map using the difference map and a threshold reference curve indicating an adaptive threshold. For this purpose, the motion map has the same pixels as the input frame image, and has a value indicating the presence or absence of motion for each pixel.

[0056] Meanwhile, in another embodiment of the present invention, threshold processing may be performed not in units of pixels but in units of masks including a number of adjacent pixels. For example, a mask including 3*3, 9 pixels surrounding a central pixel may be set, and the presence or absence of motion of the central pixel may be determined by comparing the average or median value of the pixels in the mask with a threshold. In this manner, motion may be determined in units of masks while moving from one mask to another, and a motion map may be generated based on the motion determination. By performing threshold processing in units of masks, when noise is included in a particular pixel, the noise may be reduced.

[0057] As described above, the threshold processing unit 15 determines whether or not there is motion for each pixel and assigns different values ​​to pixels determined to have motion and pixels determined to have no motion, so that every pixel in the difference map has one of two predefined values, for example, 0 or 1. The map obtained in this way contains information regarding the presence or absence of motion for every pixel, and in this sense this map can be called a motion map.

[0058] Figure 6 shows an example of a motion map output when a hand phantom moves from right to left in a previous frame image and a current frame image. Figure 6(a) shows a previous frame image including a hand phantom, and Figure 6(b) shows a current frame image in which the hand phantom has moved from right to left. Figure 6(c) shows an output motion map showing the motion detection results for each pixel of the difference data.

[0059] Referring to (c) of Figure 6, the areas displayed in white indicate pixels where motion was detected, and it can be seen that areas in the hand phantom where there is actual motion that are highly contrasted with the previous frame (the periphery of the bone area) are detected as motion, while areas where there is little contrast with the previous frame (skin area, tool area) are not detected as motion. As a result, when the final output image is generated in the subsequent process, the areas with motion are updated around the current frame, and the remaining areas are averaged, which reduces noise, strengthens edges, improves sharpness, and prevents motion blur.

[0060] The cumulative motion map generating unit 16 accumulates motion maps in a separate memory in time order based on the motion detection result, and adds them together to generate a cumulative motion map, which is used to determine an appropriate blending ratio of a previous frame image and a current frame image to generate a final output image.

[0061] FIG. 7 is a diagram showing a method of generating a cumulative motion map using a motion map. The cumulative motion map generating unit 16 may generate and update a cumulative motion map by accumulating and storing the motion map obtained through the motion detection in a separate independent memory for each frame in chronological order. The image on the left side of FIG. 7 shows a cumulative motion map, and the image on the right side shows a cumulative motion map obtained by adding up the motion maps. The cumulative motion map of the current frame may be generated by adding up the values ​​of the same pixel in the cumulative motion map of the previous frame and the motion map of the current frame. That is, the cumulative motion map of a particular frame is an image obtained by adding up the pixel values ​​of the same pixel in all the motion maps obtained up to that frame. For example, referring to FIG. 7, if the current frame is the fourth frame, the sum of the values ​​of the same pixel in the motion maps obtained from the four frames up to the present becomes the value of the same pixel in the cumulative motion map of the current frame.

[0062] If a pixel determined to have motion in a motion map is brightened by setting its value to "0" and a pixel determined to have no motion is darkened by setting its value to "1", each pixel in the cumulative motion map has a value corresponding to the sum of the motion detection values ​​(0 or 1) of the pixel, and has a different value, i.e., different brightness, depending on the number of motion detections. For example, assuming a cumulative motion map consisting of 10 frames, the cumulative motion map is obtained by adding up the 10 motion maps, and each pixel in the cumulative motion map has one of the values ​​between 0 and 10. Here, if the values ​​of the same pixel in all the motion maps are all 0, the pixel in the cumulative motion map has a value of 0, and if the values ​​of the same pixel in all the motion maps are all 1, the pixel in the cumulative motion map has a value of 10. Thus, as shown in FIG. 8, each pixel in the cumulative motion map has a pixel value, i.e., brightness, depending on the number of motion detections of the same pixel in the motion map up to the frame.

[0063] In this sense, the brighter the pixel value in the cumulative motion map, the higher the motion probability, and the darker the pixel value, the lower the motion probability. For example, if motion is detected at the same pixel position while frames are progressing in chronological order, the motion probability is high, and if motion is not detected, the motion probability is low.

[0064] The value of each pixel in the cumulative motion map indicates the degree of motion of that pixel, the motion probability value. In other words, if the motion map of each frame assigns a judgment value of "0" if there is motion and "1" if there is no motion, the smaller the pixel value in the cumulative motion map, the greater the motion probability of that pixel.

[0065] Meanwhile, in another embodiment of the present invention, instead of accumulating pixel values ​​of the motion map when updating the cumulative motion map, the cumulative motion map may be updated by selectively increasing or decreasing pixel values ​​of pixels of the cumulative motion map using a thresholded statistical value map.

[0066] An example of a process of generating a motion map and a cumulative motion map according to an input image as frames progress is shown in Fig. 8. A motion map is generated based on the difference between the frame image of each frame and the previous frame image, and a cumulative motion map for the current frame is generated by combining the generated motion map with the cumulative motion map for the previous frame.

[0067] Meanwhile, according to an embodiment of the present invention, the entropy map generating unit 18 generates a statistical value map for adaptive thresholding used to generate a motion map. The statistical value map is generated from the cumulative motion map and is formed to have the same number of pixels as the cumulative motion map. Each pixel of the statistical value map has a statistical value indicating the probability of motion occurring at the pixel in the cumulative motion map as a pixel value. For example, a mask including a plurality of pixels including the pixel in the cumulative motion map may be set as the statistical value of the pixel, indicating the probability of motion occurring in the mask. For example, the statistical value indicating the probability of motion occurring at a specific pixel may be an entropy value, standard deviation, etc. of pixel values ​​of a plurality of pixels surrounding the specific pixel. Hereinafter, a case where entropy is used as a statistical value will be described as an example, and in this sense, the statistical value map is referred to as an entropy map. The entropy map is generated from the cumulative motion map by utilizing the concept of entropy, which is a statistical approach. The generated entropy map may be stored in a separate buffer memory and used to generate a motion map for the next frame. The motion map of the current frame can be generated using the entropy map generated in the previous frame, and the motion map of the current frame can be generated again using the entropy map generated in the current frame when necessary. In this case, the motion map of the current frame can be generated using not only the entropy map of the immediately preceding frame, but also one or more entropy maps of the previous frame.

[0068] Since the motion map is generated based on the difference map obtained by the difference between the current frame image and the previous frame image, the motion of the object may not be accurately detected in some cases. For example, if the object is thin, it may be difficult to detect the motion if the difference value, i.e., the pixel value of each pixel of the difference map, is not sufficiently larger than the noise level. For example, when a thin and sharp object such as a needle passes through a thick part of a patient, it may be difficult to detect the motion of the needle. To solve this problem, in an embodiment of the present invention, a motion map is generated by applying a threshold having a different value for each pixel according to conditions through adaptive thresholding.

[0069] FIG. 9 shows a motion map and an output image obtained when a thin rod-shaped lead passes over a stationary pelvis phantom, and this image was obtained without applying the adaptive threshold processing according to an embodiment of the present invention. FIG. 9(a) shows a motion map, and FIG. 9(b) shows an output image. The arrows shown in FIG. 9(b) indicate the direction of motion. Referring to FIG. 9, it can be seen that in the region where the rod passes through, in the thin part of the phantom, the difference value before and after the rod passes is large enough, so that it is well detected as a pixel with motion, but when it passes through a thick bone region, the difference value is not large enough, so that it is not detected as a moving pixel. In this case, the pixel is determined to be noise and is averaged when the output image is generated, and as a result, a force image is generated in the form of the rod being cut.

[0070] Figure 10 shows a motion map obtained under the same conditions as Figure 9. Figure 10(a) shows a motion map when the threshold for motion detection is increased, and Figure 10(b) shows a motion map when the threshold is lowered. As shown in Figure 10(b), when the threshold for motion detection is lowered so that the stick does not break, the noise is regarded as moving pixels, resulting in a side effect of emphasizing the noise. Thus, when detecting the motion of a thin object, there is a trade-off depending on the threshold.

[0071] In order to solve this problem, the embodiment of the present invention utilizes the concept of entropy, which is a statistical approach. Entropy is a numerical representation of the confidence or amount of information contained in a probability distribution. If the probability of a specific value occurring in a probability distribution is high and the probability of the remaining values ​​is low, the entropy is small, and conversely, if the probabilities of various values ​​occurring are similar, the entropy is high. Entropy can be considered as one of the characteristic values ​​that indicate the distribution characteristics of a probability distribution. If the probability or probability density is concentrated at a specific value, the entropy can be said to be small, and conversely, if it is spread evenly among various values, the entropy can be said to be large. In other words, the larger the entropy, the more heterogeneous and the more information there is, and the smaller the entropy, the more homogeneous and the less information there is.

[0072] As is well known, in statistics, entropy (H) means the average amount of information, and can be defined for a discrete random variable X as follows:

[0073]

number

[0074] The entropy of the motion probability value may be calculated for each pixel or for each set of pixels in the cumulative motion map, and the calculated entropy may be assigned to each pixel or a set of pixels to generate an entropy map. For example, a masking area may be selected for all pixels in the cumulative motion map, the entropy value of the motion probability value of the pixel belonging to the masking area may be calculated, and the calculated entropy value may be assigned as the pixel value of the pixel to generate an entropy map. A sufficiently large area including a pixel to be calculated may be taken as a mask, and the entropy may be calculated using a histogram for the motion probability value in the mask. In this case, the histogram may be calculated by setting the motion probability value as a frequency and the number of occurrences of each motion probability value as a frequency, and the entropy may be calculated based on this using Equation 1. For example, if the cumulative motion map is a 1500*1500 image, a 300*300 mask including a center pixel to be calculated may be set, and the entropy in the set mask may be calculated and set as the entropy of the center pixel. FIG. 11 shows an example of an entropy map generated by calculating the entropy in a masking operation of pixels in a cumulative motion map. FIG. 11(a) shows an example of a cumulative motion map, with the masks shown by dotted lines. The entropy is calculated for each mask, and it can be predicted that the rightmost mask of the three masks shown in FIG. 11(a) contains the most bright pixels, i.e., pixels with a high motion probability, and thus the entropy of the pixels belonging to the rightmost mask is the largest. It can also be predicted that the leftmost mask in FIG. 11(a) contains the most dark pixels, i.e., pixels with a low motion probability, and thus the entropy of the pixels belonging to the leftmost mask is the smallest. FIG. 11(b) shows an example of an entropy map generated by the entropy calculated for the masks set for all pixels in the cumulative motion map in (a).Thus, each pixel of the entropy map has the entropy value of the motion probability value of the pixels belonging to the mask including the pixel as a pixel value. At this time, a pixel or area with a large entropy can be considered to indicate a large probability of motion, and a pixel or area with a small entropy can be considered to indicate a small probability of motion. In this sense, entropy can be considered as a measure of the congestion of the motion probability value.

[0075] In an embodiment of the present invention, the entropy map thus generated is used to generate a motion map, and a threshold for motion detection is adjusted using the entropy of each pixel included in the entropy map. The generated motion map is stored in a separate buffer memory, and adaptive threshold processing is performed based on this entropy map, i.e., the entropy map generated in the previous frame, when generating a motion map for the next frame. The threshold for each pixel for threshold processing performed by the threshold processing unit can be set to be inversely proportional to the magnitude of entropy of the corresponding pixel in the entropy map. For example, when generating a motion map, a pixel or area with high entropy has a high probability of having moved in the past, so that the motion detection threshold can be lowered to detect motion more sensitively, and in contrast, a pixel or area with low entropy has a low probability of having moved in the past, so that the motion detection threshold can be raised to detect motion conservatively. Through such adaptive threshold processing, the trade-off problem according to the magnitude of the threshold mentioned above can be improved. Meanwhile, when variance or standard deviation is used as a statistical value instead of entropy, a pixel or area with a small variance or standard deviation can be considered to have a high probability of having moved in the past.

[0076] In this way, by generating a motion map through adaptive threshold processing based on entropy, the accuracy of motion detection for each frame is increased. Even if accurate motion detection is not performed for each frame, the amount of information accumulates as the frames progress, and the area gradually expands in the direction of lowering the threshold of the surrounding pixels of the moving object. As a result, the accuracy of motion detection for thin objects is greatly improved as the frames progress.

[0077] 12 shows a method for generating a motion map according to another embodiment of the present invention. Although the motion map is generated through threshold processing according to the noise level of the image in the above embodiment, noise components may remain in the motion map, so in this embodiment, an additional noise reduction process is performed.

[0078] In this embodiment, after separating the object from the background, the remaining noise is removed by excluding the background area from the motion map. A background image from which the object has been removed is generated by applying a threshold value experimentally or through AI learning in the current frame image, and a motion map from which noise components have been removed is generated by adding or multiplying the motion map generated according to the above embodiment and the generated background image. In this way, noise components remaining in the background are removed, and a motion map containing only information on the motion of the object can be generated. This method has the advantage that when the motion detection threshold is lowered, the noise level of the entire image increases slightly, while the motion of the object can be detected sensitively.

[0079] Meanwhile, in another embodiment of the present invention, when setting a threshold for motion detection, a background and a subject area may be distinguished and different thresholds may be applied to generate two motion maps, and then the two generated motion maps may be merged to generate a final motion map.

[0080] The output image generating unit 17 mixes the current frame image and the previous frame image to generate an output image. At this time, the output image generating unit 17 mixes the current frame image and the previous frame output image at an appropriate mixing ratio based on the cumulative motion map to generate a final output image. The reflection ratio of the current frame image may be set to be relatively large as the value of each pixel in the cumulative motion map indicates motion. In other words, the mixing ratio may be determined such that the weight of the current frame increases as the motion probability determined by the pixel value included in the cumulative motion map increases. For example, the reflection ratio may be determined such that the reflection ratio of the current frame image increases linearly as the pixel value of the cumulative motion map indicates motion on a pixel-by-pixel basis.

[0081] As a specific example, for pixels with a lot of motion, a predetermined weight (α1), for example 0.8, is assigned to the pixel of the current frame image, and a weight (1-α1), for example 0.2, is assigned to the pixel of the accumulated output image up to the previous frame, and this mixing process is performed for all pixels to mix the current frame image and the previous frame output image. Meanwhile, for pixels with little motion, a predetermined weight (α2), for example 0.2, is assigned to the pixel of the current frame image, and a weight (1-α2), for example 0.8, is assigned to the pixel of the accumulated previous frame image to mix the current frame image and the previous frame output image. If the weight of the current frame is high for pixels with a lot of motion, an image without motion blur can be obtained. As a result, the final output image is updated to reflect more of the current frame value for pixels with a lot of motion based on the motion probability, and updated to reflect more of the previous frame value for pixels with little motion, and noise reduction performance can be improved as frames accumulate over time.

[0082] After the final noise-reduced image is output, the result is stored in a memory, and when the output image of the next frame is generated, the result is used for the previous frame image, thereby implementing a recursive method.

[0083] 13 shows the process of an image processing method according to another embodiment of the present invention. As shown in FIG. 13, the entropy map generated by the cumulative motion map is used when generating an output image by mixing the current frame image and the previous frame image. As in the above embodiment, the mixing ratio of the current frame image and the previous frame image is determined by the cumulative motion map, and a pixel with a high entropy value means that there is a high probability that motion has occurred in the previous frame batch.

[0084] Through threshold processing of the entropy map, pixels where motion is determined to have occurred in the entropy map can be determined. At this time, the threshold for threshold processing can be determined experimentally in advance, or the sensitivity can be set while observing the image quality displayed in real time through a user interface. For example, when generating an entropy map, if the mask size for entropy calculation is determined, the maximum value that the entropy can have is determined, and the lower 20% value of the maximum entropy can be set as the threshold.

[0085] In this embodiment, the entropy map is thus thresholded and the thresholded entropy map is used to update the cumulative motion map, as illustrated in FIG.

[0086] Although an embodiment of the present invention has been described above, the scope of the present invention is not limited thereto, and includes all changes and modifications that can be easily modified from the embodiment of the present invention by a person having ordinary knowledge in the technical field to which the present invention belongs and are recognized as equivalent. [Industrial Applicability]

[0087] INDUSTRIAL APPLICABILITY The present invention relates to a method for processing images obtained by radiation photography, and is applicable to image processing technology, and therefore has industrial applicability.

Claims

1. An image processing device that performs noise reduction processing on an image obtained by radiation imaging using a recursive method, an image input unit for receiving an input of frame-by-frame images obtained by the radiation imaging; a motion map generating unit for generating a motion map including motion sensing information for each pixel of a difference map obtained by subtracting a current frame image from a previous frame image; a cumulative motion map generating unit for generating a cumulative motion map based on the generated motion map and a motion map accumulated up to a previous frame; a statistical map generating unit for generating a statistical map formed by assigning to each pixel of the cumulative motion map a statistical value indicative of a motion probability of the each pixel; an output image generating unit for generating an output image of the current frame by mixing the current frame image and the output image of the previous frame based on the cumulative motion map.

2. The video processing device of claim 1 , wherein the motion map generation unit is configured to generate the motion map by utilizing the statistical value map.

3. The motion map generation unit includes: a difference map generating unit for generating the difference map according to a difference between the current frame image and the previous frame image; The image processing apparatus of claim 1 , further comprising: a thresholding unit configured to generate the motion map including the motion sensing information for each pixel by thresholding a pixel value of each pixel of the difference map.

4. The image processing device of claim 3 , wherein the thresholding unit is configured to threshold the difference map through an adaptive threshold whose magnitude depends on the magnitude of a statistic of the statistical value map.

5. The image processing device of claim 3 , wherein the threshold processing unit is configured to set a lower threshold for each pixel for the threshold processing as the statistical value of the corresponding pixel in the statistical value map indicates a larger motion probability.

6. The image processing device of claim 1 , wherein the statistical value map generating unit is configured to calculate the statistical value in units of a pixel mask including a plurality of pixels including each pixel of the cumulative motion map, and assign the calculated statistical value as a statistical value for each pixel to generate the statistical value map.

7. The image processing device of claim 6 , wherein the statistical value is an entropy, a variance, or a standard deviation of pixel values ​​of a plurality of pixels of the pixel mask.

8. The image processing device of claim 1 , wherein the output image generating unit is configured to determine a blending ratio of the current frame image and the output image of the previous frame based on a cumulative motion map updated using the statistical value map.

9. 9. The image processing device of claim 8, wherein the cumulative motion map generation unit is configured to perform a thresholding process on the statistical value map and to selectively increase or decrease pixel values ​​of pixels of the cumulative motion map using the thresholded statistical value map to update the cumulative motion map.

10. The image processing device of claim 1 , wherein the difference map is obtained using at least one of the current frame image and a previous frame image.

11. The image processing device according to claim 1 , wherein the previous frame image is an output image of the previous frame.

12. The image processing device of claim 1 , wherein the cumulative motion map generating unit is configured to update the cumulative motion map by using the statistical value map.

13. The image processing device of claim 12 , wherein the output image generating unit is configured to determine a blending ratio of the current frame image and the output image of the previous frame based on the updated cumulative motion map.

14. 1. An image processing method for performing a recursive noise reduction process on an image obtained by radiation imaging, comprising: receiving an input of frame-by-frame images obtained by the radiation imaging; generating a motion map including motion sensing information for each pixel of a difference map obtained by subtracting a current frame image from a previous frame image; generating an accumulated motion map based on the generated motion map and a motion map accumulated up to a previous frame; generating a statistical value map by assigning to each pixel of the cumulative motion map a statistical value indicative of a motion probability of a plurality of pixels including the pixel; generating an output image of the current frame by blending the current frame image and the output image of the previous frame based on the cumulative motion map.

15. The image processing method of claim 14 , wherein in the generating the motion map, the motion map is generated using the statistical value map.

16. 15. The image processing method of claim 14, wherein, in the motion map generating step, the motion map including the motion sensing information of each pixel of the difference map is generated by thresholding pixel values ​​of the pixel of the difference map, and the thresholding is performed through an adaptive threshold whose magnitude varies depending on the magnitude of the statistical value of the statistical value map.

17. 15. The image processing method of claim 14, wherein, in the motion map generating step, the motion map including the motion sensing information of each pixel is generated by thresholding pixel values ​​of each pixel of the difference map, and the threshold value for each pixel for the thresholding is set lower as the motion probability indicated by the statistical value of the corresponding pixel of the statistical value map increases.

18. 15. The image processing method of claim 14, wherein in the generating the statistical map, the statistical map is generated by calculating the statistical values ​​in units of a pixel mask including a plurality of pixels including each pixel of the cumulative motion map, and allocating the statistical values ​​as the statistical values ​​of the respective pixels.

19. The image processing method of claim 14 , wherein the statistic is an entropy, a variance, or a standard deviation of pixel values ​​of a plurality of pixels of the pixel mask.

20. The generating of the output image comprises: updating the cumulative motion map using the statistical value map; 15. The image processing method of claim 14, further comprising: determining a blending ratio of the current frame image and the output image of the previous frame based on the updated cumulative motion map.

21. 21. The image processing method of claim 20, wherein, in updating the cumulative motion map, the statistical value map is thresholded, and the cumulative motion map is updated by selectively increasing or decreasing pixel values ​​of pixels of the cumulative motion map using the thresholded statistical value map.

22. The image processing method of claim 14 , wherein the difference map is obtained using at least one of the current frame image and a previous frame image.

23. The image processing method according to claim 14, wherein the difference map is obtained by a difference between the current frame image and the output image of a previous frame.

24. The image processing method of claim 14 , further comprising updating the cumulative motion map using the statistical value map.

25. The image processing method of claim 24, wherein in generating the output image, a mixing ratio of the current frame image and the output image of the previous frame is determined based on the updated cumulative motion map.

Images