WO2017056600A1

WO2017056600A1 - Image processing method and control program

Info

Publication number: WO2017056600A1
Application number: PCT/JP2016/069136
Authority: WO
Inventors: ジャリコバンステーンベルグ
Original assignee: 株式会社Ｓｃｒｅｅｎホールディングス
Priority date: 2015-09-28
Filing date: 2016-06-28
Publication date: 2017-04-06
Also published as: JP2017068349A

Abstract

The purpose of the present invention is to provide a technology which segments an image into a plurality of regions on the basis of feature values, with which it is possible to alleviate effects which defects have on the results of the segmenting into the regions, without changing processing methods depending on the type of defect. Provided is an image processing method, comprising a first step (S105-S106) in which an image is segmented into a plurality of blocks and feature values are derived for each block, a second step (S107) in which the feature values for each block are corrected on the basis of the feature values of a plurality of peripheral blocks which are in the periphery of a given block, and a third step (S108) in which the image is region segmented into a plurality of regions according to the corrected feature values of each block.

Description

Image processing method and control program

The present invention relates to an image processing method for dividing an image into a plurality of regions based on feature value.
Cross-reference to related applications The disclosures in the specification, drawings, and claims of the following Japanese application are incorporated herein by reference in their entirety:
Japanese Patent Application No. 2015-189844 (filed on Sep. 28, 2015).

In the area division process in which the image is divided into a plurality of areas according to the feature amount obtained from the image, partial defects such as noise, dirt, and distortion mixed in the image may affect the processing result. For this reason, it is necessary to remove such defects from the image. As a technique aiming at removing defects such as noise from an image, there is one described in Patent Document 1, for example. In this technology, the gradation level difference caused by highlights is eliminated by performing a correction process that multiplies the luminance value of the target pixel by a coefficient corresponding to the difference between the average value of the luminance values of the surrounding pixels. Has been. As described above, an attempt is generally made to eliminate the defect by correcting the pixel value (luminance value) of the pixels constituting the image.

JP 2011-022656 A

The above prior art is specialized in eliminating gradation steps due to highlights, but when correcting the image itself as described above, it is necessary to prepare a correction method according to the type of defect. This is because the influence on the feature value varies depending on the type of defect. For this reason, for example, in an image including a plurality of types of defects, individual correction processing is required for each type of defect. As a result, there arises a problem that the time required for processing becomes long, or image information necessary for calculating the feature amount is lost due to excessive correction. In addition, it is difficult to predict in advance what kind of defect will appear in the image.

The present invention has been made in view of the above problems, and in the technology for dividing an image into a plurality of regions based on the value of the feature amount, it is not necessary to change the processing method according to the type of defect, and the defect is a result of the region division. The purpose is to provide a technology capable of suppressing the influence.

In one aspect of the image processing method according to the present invention, in order to solve the above-described problem, a first step of obtaining a feature amount for each block obtained by dividing an image into a plurality of blocks, and a feature amount for each block And a second step of correcting the image based on the feature value values of the plurality of neighboring blocks, and a third step of dividing the image into a plurality of regions according to the feature value values of the corrected blocks. Yes.

The invention thus configured does not correct the image itself by correcting the luminance value of the image. That is, correction is performed on the feature amount obtained from the image from the viewpoint of favorably performing region division based on the feature amount value. Specifically, the feature value for each block obtained by dividing the image into a plurality of blocks is corrected based on the feature value of the blocks around the block.

The feature value calculated for each block may include a contribution due to a defect included in the image in the block. Such a contribution is considered to appear in the surrounding blocks containing the same defect as well. Therefore, it is possible to reduce the influence of the defect by performing correction based on the feature value of the peripheral block with respect to the feature value of the target block to be corrected. Even when the influence on the value of the feature amount varies depending on the type of defect, the correction is performed using the feature amount values including the influence, so that it is not necessary to change the correction method according to the type of defect.

According to another aspect of the present invention, a first step of obtaining a feature amount for each block in which an image is divided into a plurality of blocks, and a feature amount for each block are calculated as feature amounts of a plurality of peripheral blocks around the block. A control program that causes a computer to execute a second step of correcting based on a value and a third step of dividing an image into a plurality of regions in accordance with the value of the feature value for each corrected block. The series of processes described above can be executed using hardware resources provided in a computer device having a general configuration. By providing the present invention as a control program executable by such a computer, it is possible to cause the computer to execute the processing of the present invention.

As described above, in the present invention, correction based on the feature values of the peripheral blocks is performed on the feature value obtained for each block by dividing the image into a plurality of blocks instead of the luminance value of the image. Therefore, it is possible to reduce the influence of the defect on the feature value regardless of the type of defect. As a result, it is possible to suppress the influence of defects included in the image on the result of area division.

The above and other objects and novel features of the present invention will become more fully apparent when the following detailed description is read with reference to the accompanying drawings. However, the drawings are for explanation only and do not limit the scope of the present invention.

It is a figure which shows schematic structure of the imaging device which can apply the image processing method concerning this invention. It is a 1st figure which shows typically the example of the image imaged with this imaging device. It is a 2nd figure which shows the example of the image imaged with this imaging device typically. It is a figure explaining the principle of the correction process in this embodiment. It is a 1st figure for demonstrating the specific content of a correction process. It is a 2nd figure for demonstrating the specific content of a correction process. It is a flowchart which shows the image processing content performed by this imaging device.

FIG. 1 is a diagram showing a schematic configuration of an imaging apparatus to which an image processing method according to the present invention can be applied. This imaging device 1 is cultured in a liquid injected into a recess called a well W formed on the upper surface of a well plate WP. ).

The well plate WP is generally used in the fields of drug discovery and biological science. On the upper surface of the flat well plate WP, a plurality of wells W having a substantially circular cross section and a transparent bottom surface are provided. Although the number of wells W in one well plate WP is arbitrary, for example, 96 (12 × 8 matrix arrangement) can be used. The diameter and depth of each well W is typically about several mm. The size of the well plate and the number of wells targeted by the imaging apparatus 1 are not limited to these and are arbitrary. For example, those having 12 to 384 holes are generally used. The imaging apparatus 1 can be used not only for imaging a well plate having a plurality of wells but also for imaging cells or the like cultured in a flat container called a dish.

A predetermined amount of a liquid as a medium is injected into each well W of the well plate WP, and cells or the like cultured under a predetermined culture condition in the liquid are imaging targets of the imaging apparatus 1. The medium may be one to which an appropriate reagent has been added, or it may be in a liquid state and gelled after being placed in the well W. As will be described later, in the imaging apparatus 1, for example, cells cultured on the inner bottom surface of the well W can be targeted for imaging. Commonly used liquid volume is about 50 to 200 microliters.

The imaging apparatus 1 includes a holder 11 that holds the well plate WP, an illumination unit 12 that is disposed above the holder 11, an imaging unit 13 that is disposed below the holder 11, and a CPU 141 that controls the operations of these units. The control part 14 which has. The holder 11 is in contact with the peripheral edge of the lower surface of the well plate WP that holds the sample together with the liquid in each well W to hold the well plate WP in a substantially horizontal posture.

The illumination unit 12 emits appropriate diffused light (for example, white light) toward the well plate WP held by the holder 11. More specifically, for example, a combination of a white LED (Light-Emitting-Diode) light source as a light source and a diffusion plate can be used as the illumination unit 12. The illumination unit 12 illuminates cells and the like in the well W provided on the well plate WP from above.

An imaging unit 13 is provided below the well plate WP held by the holder 11. The imaging unit 13 is provided with an imaging optical system (not shown) at a position directly below the well plate WP. The optical axis of the imaging optical system is oriented in the vertical direction. FIG. 1 is a side view, and the vertical direction in the figure represents the vertical direction.

The cell etc. in the well W are imaged by the imaging unit 13. Specifically, light emitted from the illumination unit 12 and incident on the liquid from above the well W illuminates the imaging target. Light transmitted downward from the bottom surface of the well W is incident on a light receiving surface of an image pickup device (not shown) through the image pickup optical system. An image of the imaging target imaged on the light receiving surface of the imaging device by the imaging optical system is captured by the imaging device. As the image sensor, for example, a CCD sensor or a CMOS sensor can be used, and either a two-dimensional image sensor or a one-dimensional image sensor may be used.

The imaging unit 13 can be moved in the horizontal direction and the vertical direction by a mechanical control unit 146 provided in the control unit 14. Specifically, the mechanical control unit 146 moves the imaging unit 13 in the horizontal direction based on a control command from the CPU 141. Thereby, the imaging unit 13 moves in the horizontal direction with respect to the well W. The focus is adjusted by moving in the vertical direction. When the imaging target in one well W is imaged, the mechanical control unit 146 positions the imaging unit 13 in the horizontal direction so that the optical axis coincides with the center of the well W. When the imaging device of the imaging unit 13 is a one-dimensional image sensor, a two-dimensional image is captured by scanning the imaging unit 13 in a direction orthogonal to the longitudinal direction of the image sensor. With such an imaging method, it is possible to perform non-contact, non-destructive and non-invasive imaging on cells or the like that are imaging targets, and suppress damage to cells or the like due to imaging.

Further, when moving the imaging unit 13 in the horizontal direction, the mechanical control unit 146 moves the illumination unit 12 integrally with the imaging unit 13 as indicated by a dotted arrow in the drawing. That is, the illuminating unit 12 is arranged so that the optical center thereof substantially coincides with the optical axis of the imaging unit 13, and moves in conjunction with the imaging unit 13 when moving in the horizontal direction. Thereby, no matter which well W is imaged, the center of the well W and the light center of the illumination unit 12 are always located on the optical axis of the imaging unit 13. Therefore, it is possible to maintain the imaging condition favorably while keeping the illumination condition for each well W constant.

The image signal output from the image sensor of the imaging unit 13 is sent to the control unit 14. That is, the image signal is input to an AD converter (A / D) 143 provided in the control unit 14 and converted into digital image data. The CPU 141 executes image processing as appropriate based on the received image data. The control unit 14 further includes an image memory 144 for storing and storing image data, and a memory 145 for storing and storing programs to be executed by the CPU 141 and data generated by the CPU 141. It may be integral. The CPU 141 executes various control processes described later by executing a control program stored in the memory 145.

Besides, the control unit 14 is provided with an interface (IF) unit 142. The interface unit 142 accepts operation input from the user and presents information such as processing results to the user, and exchanges data with an external device connected via a communication line. It has a function. In order to realize the user interface function, the interface unit 142 is connected to an input receiving unit 147 that receives an operation input from the user and a display unit 148 that displays and outputs a message to the user, a processing result, and the like.

The control unit 14 may be a dedicated device equipped with the hardware described above, and a control program for realizing processing functions to be described later is incorporated into a general-purpose processing device such as a personal computer or a workstation. It may be. That is, a general-purpose computer device can be used as the control unit 14 of the imaging device 1. In the case of using a general-purpose processing device, it is sufficient that the imaging device 1 has a minimum control function for operating each unit such as the imaging unit 13.

FIG. 2A and FIG. 2B are diagrams schematically showing examples of images picked up by this image pickup apparatus. Specifically, FIG. 2A shows an example of an ideal image picked up by the image pickup apparatus 1, and FIG. 2B shows an example of an image including a defect. Hereinafter, the horizontal direction of the image captured by the imaging unit 13 is referred to as “X direction”, and the vertical direction is referred to as “Y direction”. As shown in FIG. 2A, the image Ia is imaged so that the whole well W fits in a rectangular region corresponding to the imaging field of view of the imaging unit 13. In the image Ia, in the substantially circular region Rw corresponding to the inside of the well W, cells and the like C are scattered in the medium M distributed substantially uniformly.

As will be described later, in the imaging apparatus 1, the well region Rw is divided into a region of the medium M and a region of cells C, etc. by an appropriate region dividing process using the feature value calculated for each pixel constituting the image. Divided. However, as shown in FIG. 2B, the actually captured image Ib may include some image defect D. Image defects can occur due to various causes such as noise, dirt, distortion, and the like.

Such a defect may affect the calculation of the feature value and the result of the region division processing based on the result. Therefore, correction processing is required to reduce the influence of defects appearing in the image on the region division processing. Hereinafter, correction processing in this embodiment will be described.

FIG. 3 is a diagram for explaining the principle of correction processing in this embodiment. The upper part of FIG. 3 schematically shows a part of an image including the defect D like the image Ib shown in FIG. 2B. Consider a case where, for example, a feature amount F related to edge strength is obtained from this image Ic. The lower part of FIG. 3 schematically shows how the value of the feature value F obtained for each pixel located on the line segment AA in the image Ic changes depending on the position. As shown in the figure, the value of the feature amount F is remarkably increased in the pixel corresponding to the outline of the cell C or the like, and a value different from the other portion appears in the portion corresponding to the defect D.

Note that here, the value of the feature amount F is smaller in the portion of the defect D than in the other portions. However, depending on the type of the defect D and the type of the feature amount F, the value may be larger than the other portions or there may be almost no difference.

It is considered that the influence on the feature amount due to the defect D appears almost the same between the pixels corresponding to the defect D. Therefore, like the values F1 and F2 shown in the figure, the value of the feature amount of the target pixel is set, for example, in the vicinity thereof (more specifically, an appropriate number selected in order from the closest distance to the target pixel). By expressing the value of the feature amount of the pixel as a relative value, the feature amount that is not affected by the presence or absence of the defect D can be obtained. This corresponds to correcting the feature value of the pixel of interest based on the feature value of other pixels around the pixel. Since the actual image has a two-dimensional spread, the specific contents of the correction processing are as follows.

4A and 4B are diagrams for explaining specific contents of the correction processing. More specifically, FIG. 4A is a diagram showing a relationship between a pixel of interest and peripheral pixels used for correction. FIG. 4B is a diagram showing the value of the feature amount F used for correction. As shown in FIG. 4A, N pixels (N is an integer of 3 or more) in the X direction and the Y direction centering on the pixel of interest P (x, y) whose X coordinate value is x and Y coordinate value is y. A rectangular region Rr having a size of The feature value of the pixel of interest P (x, y) is corrected using the feature value of the pixel in the region Rr. Hereinafter, each pixel in the region Rr excluding the target pixel P (x, y) is referred to as a “peripheral pixel” for the target pixel P (x, y).

The pixel at the upper left corner of the rectangular area Rr is denoted by P (x1, y1), the pixel at the upper right corner is denoted by P (x2, y1), the pixel at the lower left corner is denoted by P (x1, y2), and the pixel at the lower right corner is denoted by P. Represented by (x2, y2), respectively. here,
x1 = x− (N−1) / 2
x2 = x + (N-1) / 2
y1 = y− (N−1) / 2
y2 = y + (N-1) / 2
It is.

As shown in FIG. 4B, when the value of the feature value before correction of the pixel P (x, y) is represented by the symbol Fr (x, y), the feature value Fc () after correction of the pixel P (x, y). x, y) is:
Fc (x, y) = Fr (x, y) −Fa (x, y)
Defined by Here, Fa (x, y) is an average value of the feature values Fr (i, j) of the respective pixels P (i, j) in the rectangular region Rr, and the following formula (1) or formula (2) Represented by either

Equation (1) represents an average value of feature amounts of peripheral pixels other than the target pixel P (x, y) among the pixels in the rectangular region Rr. Expression (2) represents the average value of the feature values of all the pixels in the rectangular region Rr including both the target pixel P (x, y) and the surrounding pixels. As the average value Fa (x, y) of the feature values of peripheral pixels serving as a reference when correcting the feature value Fr (x, y) of the target pixel P (x, y), the above formulas (1) and (2 ) May be used.

In the above description, the difference between the feature value Fr (x, y) of the target pixel P (x, y) and the average value Fa (x, y) of the feature values of the surrounding pixels (and the target pixel) is corrected. The subsequent feature value Fc (x, y) is used. Furthermore, a value obtained by scaling the difference value by adding an appropriate offset value or multiplying by a coefficient may be used as the corrected feature amount. In general, the corrected feature value Fc (x, y) is expressed by the following equation using constants a, b, and c (where a ≠ 0):
Fc (x, y) = a · {Fr (x, y) −Fa (x, y) + b} + c
Can be represented by That is, the corrected feature value Fc (x, y) generally uses a difference between the feature value Fr (x, y) before correction and the average value of the feature values of the surrounding pixels (and the target pixel) as a variable. It can be defined as a linear function.

For example, when it is necessary to visualize the feature value by mapping the corrected feature value on the XY coordinate plane, the corrected feature value is appropriately scaled as described above. It becomes possible to enhance the visualization effect.

There are various possible feature quantities for which such correction functions effectively. In the experiment of the present inventor, a pixel value profile obtained by plotting the pixel value of each pixel in a three-dimensional coordinate space having the pixel value (luminance value) as a third coordinate axis orthogonal to the X coordinate and the Y coordinate is drawn. It has been confirmed that a good correction result can be obtained with a feature amount obtained as a standard deviation in the direction of the normal vector at each pixel position on the curved surface (this feature amount is often expressed as “NormalStDev”).

More generally, when the feature value can be expressed as a continuous function with the pixel value of each pixel as a variable, the above correction method based on the average value of the feature values of the surrounding pixels functions effectively. In particular, when the feature amount is expressed as a convex function or a linear function with the pixel value as a variable, a good correction result can be obtained.

FIG. 5 is a flowchart showing the contents of image processing executed by the imaging apparatus. This process is a process for distinguishing the area of cells and the area of the culture medium from the image of the well W carrying the cells and the like. This processing is realized by the CPU 141 executing a control program created in advance and stored in the memory 145 to cause each unit of the device to perform a predetermined operation. The control program may be incorporated in the memory 145 in advance, or may be read from an appropriate storage medium as needed and read into the memory 145 via the interface unit 142 and executed.

First, imaging of the well W is performed by the imaging unit 13 (step S101), and the well region Rw (for example, FIG. 2A) is cut out from the obtained original image (step S102). A well image representing the well region Rw is displayed on the display unit 148 (step S103). The content of the image displayed at this time is the same as that of the captured original image. When the original image includes a defect, the image is displayed in a state including the defect. The user can grasp the presence / absence of a defect and the extent thereof from the displayed image.

Subsequently, an operation input from the user regarding the correction size information is received by the input receiving unit 147 (step S104). The above correction algorithm itself can be applied regardless of the type and degree of the defect, but the size of the rectangular region Rr that determines the range of the peripheral pixels used for the correction needs to be appropriately selected according to the appearance of the defect. Specifically, the size of the rectangular region Rr must be larger than the size of such a texture so that information on useful textures (for example, contour lines of cells or the like) in the image is not lost. On the other hand, in order to make effective the effect of removing the influence of the defect by subtracting the average value of the feature values of the peripheral pixels, it is preferable that the defect appears to appear uniformly in the rectangular region Rr. . Then, the size of the rectangular region Rr must be smaller than the size of the region where the defect appears in the image.

In order to cope with various defects, it is preferable that the size of the rectangular region Rr is variable. For example, more effective correction can be performed by accepting information for determining the size as correction size information from the user. For example, the value of the number of pixels N that determines the length of one side of the rectangular region Rr can be received as the correction size information.

Subsequently, the well image is divided into a plurality of small blocks (step S105), and the feature amount of each block is calculated (step S106). In the above example, the feature amount is obtained in units of pixels, which corresponds to the case where one pixel is one block. Alternatively, one block may be set to include several pixels. For example, the image can be divided into blocks of a size corresponding to the size of the texture of interest in the image. When correction is performed using the feature amount obtained in units of blocks, it is desirable that the blocks have the same size.

With respect to the feature values of each block thus obtained, the feature value values of the block are corrected based on the feature value values of the surrounding blocks by the correction method described above (step S107). An appropriate region division process is performed based on the feature value of each block after correction (step S108). Thus, the well image is divided into a region corresponding to the cell C or the like and a region corresponding to the medium M. Since the influence of the image defect is reduced in the corrected feature amount, the influence of the image defect on the result of the area division process is suppressed. The region corresponding to the cell etc. C may be classified into a plurality of types according to the type and shape of the cell etc. C (for example, normal cells and undifferentiated cells etc.).

As a region segmentation method, a known machine learning method using a feature value, for example, a random forest method can be used. Alternatively, for example, a method may be used in which a threshold value in several steps is set for the corrected feature value, and the region is divided according to the magnitude relationship with the threshold value.

As described above, in the above embodiment, steps S105 and S106 in FIG. 5 correspond to the “first step” of the present invention. On the other hand, steps S107 and S108 correspond to the “second step” and “third step” of the present invention, respectively. In the above embodiment, each of the pixels constituting the image corresponds to a “block” of the present invention. Of the pixels in the rectangular region Rr, the pixels other than the target pixel P (x, y) correspond to the “peripheral block” of the present invention.

Further, among the average value Fa (x, y) of the feature quantity, the one represented by the expression (1) corresponds to the “first average value” of the present invention, and the one represented by the expression (2) is the present value. This corresponds to the “second average value” of the invention.

Note that the present invention is not limited to the above-described embodiment, and various modifications other than those described above can be made without departing from the spirit of the present invention. For example, the above embodiment is an imaging apparatus capable of executing the image processing method according to the present invention. However, an apparatus that executes the image processing method of the present invention may not have an imaging function. As described above, the image processing method according to the present invention can be executed using hardware resources of a personal computer or workstation having a general configuration. Therefore, the present invention may be realized by mounting a control program describing each processing step of the image processing method according to the present invention on such an apparatus. In this case, an image captured by an external imaging device, an image previously stored in a database or a storage medium, and the like are processing targets of the present invention.

In the above embodiment, the correction size information setting input is received from the user and the correction is executed. However, the present invention is not limited to this. For example, the configuration may be such that the degree of defect is estimated by appropriate image processing, and the size of the rectangular region Rr is automatically determined according to the result. Further, for example, there may be a mode in which several sizes of the rectangular region Rr are prepared in advance and a correction result using them is presented to the user.

In the above embodiment, each of the pixels constituting the image is a “block” of the present invention, which is the smallest in principle as a “block”. In this case, it is possible to perform correction while maintaining image information of a fine texture represented by a pixel unit included in the image. However, the block size may be larger depending on the application. For example, for the purpose of counting cells of substantially the same size scattered in the wells, a block composed of a plurality of pixels corresponding to the size of such cells may be set.

Further, in the above-described embodiment, region division processing is performed on an image obtained by imaging a well carrying cells or the like. However, the image to be processed in the present invention is not limited to the image obtained by using such a raw sample as an imaging target, and various images can be used.

As described above, the specific embodiment has been described by way of example. In the image processing method of the present invention, for example, in the second step, the value of the feature value for each block is the value of the feature value of each peripheral block of the block. You may make it correct | amend based on the 1st average value which is an average value, or the 2nd average value which is the average value of each feature-value of the said block and the surrounding block of the said block. According to such a configuration, it is possible to cancel the contribution due to defects that have the same degree of influence on the feature quantities of the block and the peripheral blocks.

Further, for example, the feature value of the block after correction in the second step may be a value corresponding to the difference between the feature value of the block before correction and the first average value or the second average value. . According to such a configuration, a value obtained by subtracting a value due to the influence of a defect from a value of the feature value of the block can be used as a corrected feature value.

In this case, the value of the feature value of the block after correction is a value represented by a linear function whose variable is the difference value between the feature value of the block before correction and the first average value or the second average value. It may be. In this way, the corrected feature value can be appropriately scaled, and the feature value in which the influence of the defect is reduced can be output in a mode according to the purpose.

Also, for example, a plurality of blocks may be the same size. According to such a configuration, the feature value of each block has the same weight as each other, and it is not necessary to give a special weight in the calculation at the time of correction, so that the processing can be simplified. .

Further, for example, in the second step, blocks other than the block that are included in a rectangle of a predetermined size including a plurality of blocks and centering on the block may be set as the peripheral blocks. In such a configuration, the correction processing is targeted for the feature amount of the block in the rectangular window set around the block, and the same processing can be performed for any block in the image. Thereby, the correction process can be simplified.

In this case, it is more preferable that the size of the rectangle can be changed. More specifically, for example, the second step may be executed in response to an input for setting information for determining the size of the rectangle. By making it possible to change the range of the peripheral blocks used for the correction process according to the appearance of defects, it is possible to further enhance the correction effect.

Further, the feature amount may be represented by a continuous function with the pixel value of each pixel constituting the image as a variable. In the feature quantity that satisfies such a condition, correction based on the value of the feature quantity of the peripheral block functions particularly effectively.

Also, for example, each of the blocks may be one of the pixels constituting the image. According to such a configuration, it is possible to make the correction function effectively for an image including a fine texture expressed in pixel units.

Although the invention has been described with reference to specific embodiments, this description is not intended to be construed in a limiting sense. Reference to the description of the invention, as well as other embodiments of the present invention, various modifications of the disclosed embodiments will become apparent to those skilled in the art. Accordingly, the appended claims are intended to include such modifications or embodiments without departing from the true scope of the invention.

The present invention can be applied to a technique for dividing an image into a plurality of regions according to the feature value, and an imaging target in an image to be processed is arbitrary.

DESCRIPTION OF SYMBOLS 1 Imaging device 13 Imaging part 14 Control part 141 CPU
147 Input reception unit 148 Display unit C cells and the like M medium S105 to S106 First step S107 Second step S108 Third step W well

Claims

A first step for obtaining a feature value for each block obtained by dividing an image into a plurality of blocks;
A second step of correcting the feature value for each block based on the feature value values of a plurality of peripheral blocks around the block;
An image processing method comprising: a third step of dividing the image into a plurality of regions in accordance with the feature value of each block after correction.
In the second step, the feature value for each block is a first average value that is an average value of the feature values of each of the peripheral blocks of the block, or each of the block and the peripheral blocks of the block. The image processing method according to claim 1, wherein correction is performed based on a second average value that is an average value of feature amounts.
The feature value of the block after correction in the second step is a value corresponding to a difference between the feature value of the block before correction and the first average value or the second average value. 3. The image processing method according to 2.
The value of the feature value of the block after correction in the second step is a linear function having a variable between the value of the feature value of the block before correction and the first average value or the second average value. The image processing method according to claim 3, wherein the image processing method is a value represented by:
5. The image processing method according to claim 1, wherein the plurality of blocks have the same size.
The said 2nd process WHEREIN: The said blocks other than the said block which are in the rectangle of the predetermined size centering on the said block including several said blocks are made into the said surrounding blocks. Image processing method.
The image processing method according to claim 6, wherein the size of the rectangle is changeable.
The image processing method according to claim 7, wherein the second step is executed in response to an input of setting information for determining the size of the rectangle.
9. The image processing method according to claim 1, wherein the feature amount is represented by a continuous function having a pixel value of each pixel constituting the image as a variable.
10. The image processing method according to claim 1, wherein each of the blocks is one of pixels constituting the image.
A first step for obtaining a feature value for each block obtained by dividing an image into a plurality of blocks;
A second step of correcting the feature value for each block based on the feature value values of a plurality of peripheral blocks around the block;
A control program for causing a computer to execute each step of the third step of dividing the image into a plurality of regions according to the corrected feature value for each block.