WO2019102750A1

WO2019102750A1 - Image processing device, image processing method, and image processing program

Info

Publication number: WO2019102750A1
Application number: PCT/JP2018/038603
Authority: WO
Inventors: 志織笹田
Original assignee: ソニー株式会社
Priority date: 2017-11-24
Filing date: 2018-10-17
Publication date: 2019-05-31

Abstract

The purpose of the present invention is to provide a new flow path assessment technique. This technique provides an image processing device that includes: a movement amount calculation unit that calculates the amount of movement between at least two images among a plurality of images obtained by imaging a flow path region in chronological succession; a feature amount acquisition unit that acquires a feature amount relating to the amount of movement for every pixel block that constitutes at least a portion of the images of the flow path region; and a flow path internal information acquisition unit that acquires flow path internal information of the flow path region on the basis of the feature amount.

Description

Image processing apparatus, image processing method, program for image processing

The present technology relates to an image processing device, an image processing method, and a program for image processing. More specifically, the present invention relates to an image processing apparatus, an image processing method, and an image processing program for acquiring flow channel internal information from a plurality of images obtained by continuous imaging of a flow channel region.

In order to evaluate a living body flow path, an evaluation method using means such as light or ultrasonic waves is used. As the said evaluation method, the method of measuring a blood flow, for example using a laser beam, and the method of acquiring the information in a blood vessel using an ultrasonic wave are mentioned. For example, means such as a blood vessel contrast agent may be used to visualize the flow path.

In addition, various image processing techniques have been developed to evaluate the movement of a living body.
For example, in Patent Document 1 below, “an image obtained by classifying each of a plurality of blocks constituting at least a part of the image data based on the feature amount of the motion specified from the image data obtained by imaging the object performing periodic motion is A data processing apparatus having a control unit for controlling to display. "(Claim 1) is described.
In Patent Document 2 below, “a motion detection unit that detects a motion of the evaluation target using an image to be evaluated, and a motion vector indicating a motion of the evaluation target detected by the motion detection unit are used. An evaluation value for evaluating the cooperativity of the movement of the evaluation object using a correlation calculation unit that calculates a correlation of temporal changes in motion amount at a plurality of evaluation objects, and the correlation calculated by the correlation calculation unit An image processing apparatus comprising: an evaluation value calculation unit to be calculated. "(Claim 1).
In Patent Document 3 below, “a motion detection unit that detects the motion of the observation target for each partial region of the observation region, a motion amount calculation unit that calculates the motion amount of each motion detected by the motion detection unit, and An image processing apparatus comprising: a map creation unit that creates a map representing the position and the size of each motion amount calculated by the motion amount calculation unit.

JP, 2015-207308, A JP, 2012-105631, A JP 2012-194168 A

Fluorescently labeled particles may be used in the assessment of biological channels. For example, fluorescent beads are allowed to flow into the blood stream to observe the flow path, and substances transported by axons are fluorescently labeled to observe axons. In such observation, although the fluorescently labeled substance is observed, the flow channel through which the fluorescently labeled substance flows may not be observable. If it is possible to evaluate the flow path through which the substance flows in the analysis using the fluorescently labeled substance, it is considered to be useful for various analysis or research.

There are also cases where the conventionally used channel evaluation method can not be applied. For example, in the case where a new flow channel is formed to generate a new flow, such as angiogenesis and nerve axonal generation, the conventionally used flow channel evaluation method may not be applicable. There are also cases where flow path visualization means such as a blood vessel contrast agent can not be applied.

Also, the image processing techniques described above are useful techniques for visualizing the motion of an object. If it is possible to evaluate the flow path through which an object flows by image processing, it is considered to be useful for various analysis or research.

The present technology aims to provide a new flow path evaluation technology.

The present inventors have found that an image processing apparatus having a specific configuration enables channel evaluation.

That is, the present technology
A motion amount calculation unit that calculates a motion amount between at least two of a plurality of images obtained by imaging the flow path region continuously in time;
A feature amount acquisition unit that acquires a feature amount related to the movement amount for each pixel block that constitutes at least a part of the image of the flow path region;
And a flow path internal information acquisition unit for acquiring flow path internal information of the flow path region based on the feature amount.
According to one embodiment of the present technology, the flow path internal information acquisition unit may include a color information addition unit that adds color information to each pixel block according to the feature amount.
According to one embodiment of the present technology, the flow passage internal information acquisition unit may further include a flow passage region image creation unit that creates an image of the flow passage region based on the color information.
According to one embodiment of the present technology, the flow channel internal information acquisition unit may further include a flow velocity acquisition unit that acquires the flow velocity inside the flow channel based on the feature quantity.
According to one embodiment of the present technology, the flow passage internal information acquisition unit may further include an abnormal area identification unit that identifies an abnormal area of the flow based on the flow velocity.
According to one embodiment of the present technology, the flow passage internal information acquisition unit may further include an area calculation unit that calculates an area of a pixel block in which the feature value is within a predetermined range.
According to one embodiment of the present technology, the feature may be obtained based on five or more motion amounts.
According to one embodiment of the present technology, the feature value may be an integrated value or an average value of the motion amounts, or a maximum value of the motion amounts.
According to one embodiment of the present technology, the plurality of images may constitute a moving image of the flow channel area.
According to an embodiment of the present technology, the at least two images may be continuous images among the images constituting the moving image According to an embodiment of the present technology, the amount of movement flows in the flow path It may be the amount of movement of particles.
According to one embodiment of the present technology, the particles may be bioparticles.
According to one embodiment of the present technology, the flow passage region may include a flow passage in a living body in which particles are flowing and / or an artificial flow passage in which particles are flowing.

Further, according to the present technology, a motion amount calculating step of calculating a motion amount between at least two images among a plurality of images obtained by imaging the flow channel region continuously in time.
A feature amount obtaining step of obtaining a feature amount related to the movement amount for each pixel block constituting at least a part of the image of the flow passage region;
There is also provided an image processing method including the flow channel internal information acquisition step of acquiring flow channel internal information of the flow channel region based on the feature amount.

Further, according to the present technology, a motion amount calculating step of calculating a motion amount between at least two images among a plurality of images obtained by imaging the flow channel region continuously in time.
A feature amount obtaining step of obtaining a feature amount related to the movement amount for each pixel block constituting at least a part of the image of the flow passage region;
There is also provided an image processing program for causing a computer to execute a flow channel internal information acquisition step of acquiring flow channel internal information of the flow channel region based on the feature amount.

The present technology provides a new image processing technology that enables channel evaluation. The technology allows, for example, visualization or quantification of angiogenesis, visualization of axonal generation, or quantification of axonal transport rate. The effects exerted by the present technology are not necessarily limited to the effects described herein, and may be any of the effects described in the present specification.

1 shows an example of an image processing system including an image processing apparatus according to the present technology. 1 is an example of a block diagram of an image processing apparatus according to the present technology. It is a figure which shows the example of moving image data. It is a figure which shows the image divided | segmented into MxN pixel block. It is a figure showing an example of a channel field picture created according to this art. 1 is an example of a block diagram of an image processing apparatus according to the present technology. It is a figure which shows the example of moving image data. It is a figure showing an example of a channel field picture created according to this art. It is a figure showing an example of the flow of the image processing method according to this art. FIG. 1 is a diagram showing an example of a schematic hardware configuration of an information processing apparatus for realizing an image processing apparatus according to the present technology. It is a figure showing an example of a channel field picture created according to this art. It is a figure which shows the example of acquisition of the feature-value from moving image data.

Hereinafter, preferred embodiments for implementing the present technology will be described. The embodiments described below show representative embodiments of the present technology, and the scope of the present technology is not limited to these embodiments. The description will be made in the following order.
1. First embodiment (image processing apparatus)
(1) Description of the First Embodiment (2) First Example of the First Embodiment (Image Processing Apparatus)
(3) Second example of the first embodiment (image processing apparatus)
2. Second embodiment (image processing method)
(1) Description of Second Embodiment (2) Example of Second Embodiment (Image Processing Method)
3. Third embodiment (program for image processing)
(1) Description of Third Embodiment Hardware configuration example

1. First embodiment (image processing apparatus)

(1) Description of the first embodiment

The present technology is directed to a motion amount calculator configured to calculate a motion amount between at least two images of a plurality of images obtained by imaging the flow passage region continuously in time, and at least an image of the flow passage region. A feature quantity acquisition unit that acquires a feature quantity related to the movement amount for each pixel block that constitutes a part; and a flow path internal information acquisition unit that acquires flow path internal information of the flow path region based on the feature quantity. An image processing apparatus is provided.

According to the present technology, it is possible to obtain flow channel internal information from an image obtained by imaging the flow channel region. For example, as the flow channel internal information, the shape or structure of the flow channel, the flow velocity in the flow channel, and / or the flow velocity distribution in the flow channel can be acquired.

Moreover, it becomes possible to evaluate the state of the flow path based on the flow path internal information acquired according to the present technology. For example, based on the information inside the flow channel acquired according to the present technology, it is possible to identify the place where the stagnation or the flow velocity decrease is occurring in the flow channel. The present technology can be applied to, for example, the following situations.

For example, when the blood vessel is observed in a state where fluorescent beads are flowed into a blood vessel (artificial blood vessel or a blood vessel in a living body), the fluorescence emitted from the bead can be observed, but the blood vessel through which the bead flows may not be observed. . According to the present technology, for example, the shape or structure of the flow channel and / or the flow velocity in the flow channel can be evaluated, so that the state of the blood vessel through which the bead flows can be grasped. For example, the place where the flow velocity of blood is decreasing, the place where blood flow is stagnant, or the place where blood leaks can be identified by the present technology. In this way, it is possible to identify where the vascular abnormality is occurring. The flow path internal information obtained according to the present technology is considered to contribute to, for example, prevention of thrombus formation in the artificial blood vessel and / or improvement of the structure of the artificial blood vessel.

For example, the technology enables visualization and / or quantification of angiogenesis. For example, it is possible by the present technology to grasp the structure or shape of the flow channel generated by angiogenesis, or the area of the flow channel generated by angiogenesis.

For example, when observing a situation in which a fluorescently labeled substance is transported in an axon, fluorescence may be observed but the structure of the axon may not be observed. The technology can, for example, assess the shape or structure or flow rate of the flow path, so that, for example, the structure of the axon can be understood, the axonal transport velocity can be quantified, and the shape or extension direction of the generated axon can be identified. It becomes possible by this technology.

For example, by applying the present technology in the case of flowing particles such as cells and beads in a microfluidic device such as a microcapillary and a microchannel chip, the flow velocity distribution in the channel can be obtained. Thus, the present technology makes it possible to identify a problem point in the flow path, for example, a place where blockage tends to occur and a place where stagnation or flow rate reduction occurs. As a result, quality evaluation of these devices becomes possible.

A more detailed description of the present technology is provided below.

In the embodiment of the present technology, the movement amount calculation unit calculates the movement amount between at least two images among a plurality of images obtained by imaging the flow passage region continuously in time. The plurality of images may be, for example, images constituting a moving image. The at least two images may be, for example, at least two of a plurality of images constituting the moving image.
The moving image may be a set of images captured sequentially in time in a region including the flow path. The frame rate of the moving image may be appropriately selected by those skilled in the art in consideration of factors such as the magnification of the imaging target and the microscope, for example. When the imaging target is a blood flow, the velocity of the blood flow is, for example, 0.05 to 50 cm / sec, so the frame rate can be selected in consideration of the velocity of the blood flow.
In addition, capturing motion of, for example, 4 to 16 pixels is considered to be efficient for image processing. Therefore, the frame rate (fps) can be set to (moving distance (pixel) per second of an object (for example, particle) moving in the image) / (4 to 16) or more. For example, when an image includes an object advancing 100 pixels per second, a frame rate of 100/4 to 100/16 fps or more may be employed, for example, a frame rate of 5 fps or more or a frame rate of 25 fps or more. You may The frame rate of the moving image may be, for example, 5 to 100 fps, preferably 10 to 80 fb, more preferably 15 to 60 fps. The frame rate can be set by selecting the type or setting of the imaging device used for imaging the flow channel region.
For example, when imaging the blood flow of a transparent animal such as zebrafish, a frame rate of 100 fps to 100 fps may be adopted, and a frame rate of 1000 fps or more may be adopted.
Examples of moving image data that can be used in the present technology are shown as 301-1 to 301-t in FIG. Thus, the moving image data may be the t-th image from the first image within a predetermined period.
In the present technology, the motion amount may be calculated over the entire time of the moving image obtained by imaging, or a certain section of the moving image, that is, a moving image of a certain time of all the time The motion amount may be calculated only for. The section of the moving image used to calculate the motion amount can be appropriately selected by those skilled in the art.

The number of images used for calculating the amount of movement may be at least 2, preferably 5 or more, more preferably 30 or more, more preferably 300 or more, and still more preferably 3000 or more. For example, when the image used to calculate the amount of movement is an image constituting a 30 fps moving image, the moving image preferably has 1 second or more, more preferably 10 seconds or more, and still more preferably 100 seconds or more for calculation of the amount of movement. It can be used. As the number of images used to calculate the amount of movement is larger, better channel internal information can be obtained.

The calculation of the motion amount can be performed by comparing the two images. The two images to be compared may be, for example, two images that are temporally continuous among a plurality of images that constitute a moving image, or may be two images that are not temporally continuous. .

Two temporally consecutive images are two images of an image obtained by imaging a predetermined area at a certain point in time and an image obtained by imaging the area at a point closest to the certain point in time. For example, it may be an image at a certain point in time that constitutes a moving image and an image immediately after the image.
For example, a one-second moving image with a frame rate of 30 fps is composed of 30 images. The amount of movement between the first and second images of the 30 images, the amount of movement between the second and third images, ..., and the 29th and 30th images The motion amount between the images of the eyes may be calculated by the motion amount calculation unit.

The two images which are not continuous in time are two images of an image obtained by imaging a predetermined area at a certain point in time and an image other than an image obtained by imaging the area at a point closest to the certain point in time. For example, it may be an image at a certain point in time that constitutes a moving image and a further image of the image immediately after the image.
For example, in the case of a 1-second moving image having a frame rate of 30 fps, the amount of movement between the first and third images of the 30 images constituting the moving image, and the third and fifth images. The amount of movement between the images,..., And the amount of movement between the 27th and 29th images may be calculated. That is, of the images constituting the moving image, the amount of movement between two images of one image skipping can be calculated. The number of images skipped when calculating the amount of movement is not limited to one. For example, the amount of movement between two images of 1 to 20 sheets, particularly 1 to 10 sheets, more particularly 2 sheets, 3 sheets, 4 sheets, 5 sheets or 10 sheets is calculated. It is also good. By reducing the number of images used for motion amount calculation, it is possible to reduce the amount of data generated by the motion amount calculation unit.

Alternatively, in the present technology, the motion amount calculation unit may calculate a motion amount between two images at predetermined two points in time among the plurality of images constituting the moving image. In the present technology, two images at predetermined two time points are, for example, two images of an image obtained by imaging a predetermined area at a certain time point and an image obtained by imaging the area after a predetermined time has elapsed from the certain time point. It is possible. For example, images every 0.01 seconds to one second, preferably every 0.1 seconds to every 0.5 seconds can be used for the movement amount calculation. More specifically, when the present technology is applied to, for example, a 10-second moving image, for example, 0.5 seconds have passed since the first image of the images constituting the 10-second moving image and the first image. The amount of movement between the second image at a point in time, the amount of movement between the second image, and the third image after 0.5 seconds from the second image, and so on An amount of movement between two images at predetermined two time points can be calculated.

In the present technology, the flow channel region may be a region having a flow channel at least in part, or may be a region having a possibility of having a flow channel in at least one portion. In the latter case, by imaging the area assumed to have a flow path and applying the present technology to the image obtained by imaging, for example, the presence or absence of the flow path in the area or the shape of the flow path Information can be obtained. The flow passage region may be, for example, a flow passage in a living body in which particles are flowing and / or an artificial flow passage in which particles are flowing. The flow path may be a structure through which a substance, such as particles, can flow. As the flow channels, for example, flow channels in a living body such as blood vessels, lymphatic vessels, and axons, artificial biological flow channels such as artificial blood vessels and artificial lymphatic vessels, and non-biological flow channels such as microchannels Although it can mention, it is not limited to these.

In the present technology, it is desirable that particles flow in the flow path to be imaged. The particles flowing in the flow channel facilitate calculation of the amount of movement between the images. That is, in the present technology, the amount of movement may be the amount of movement of particles flowing in the channel.
The particles may include living particles and non-living particles. Examples of the biological particles include various biological molecules, biological particles such as cells, microorganisms, solid components of biological origin, and liposomes. Examples of the non-living particles include, but are not limited to, non-living molecules, synthetic particles such as latex particles, gel particles, and industrial particles.
Examples of the biomolecules include, but are not limited to, molecules transported by axon and molecules flowing in the blood stream. The cells can include animal cells and plant cells. Animal cells can include, for example, tumor cells and blood cells, such as red blood cells. The microorganisms may include bacteria such as E. coli, fungi such as yeast, and the like. Examples of the living body-derived solid component include solid crystals produced in the living body.
The non-biomolecules can include reagent molecules that can be flowed into the flow channel such as blood vessels or axons. The synthetic particles may be particles made of, for example, an organic or inorganic polymer material or a metal. Organic polymeric materials may include polystyrene, styrene divinyl benzene, and polymethyl methacrylate. Inorganic polymeric materials can include glass, silica, magnetic materials, and the like. Metals may include gold colloids and aluminum. The synthetic particles can be, for example, beads, in particular fluorescent beads, intended to be flowed in the bloodstream or in the microchannel. Also, in the present technology, the particles may be a combination of a plurality of particles, such as two or three.
The particles may be labeled for enabling imaging by the imaging device. Labels can include, for example, fluorescent labels.

The lower limit of the maximum dimension of the cross section of the flow path to be analyzed in the present technology may be, for example, 1 μm, preferably 5 μm, more preferably 10 μm. The upper limit of the largest dimension of the cross section may be, for example, 10 mm, preferably 5 mm, more preferably 1 mm.
The largest dimension of the cross-section may be the diameter if the cross-section is circular, the major axis if the cross-section is elliptical, and the diagonal length if the cross-section is rectangular.

In the present technology, the motion amount is, for example, a motion vector. Motion vectors may be calculated, for example, by techniques used in motion compensated prediction coding. Examples of such techniques include, but are not limited to, gradient methods and block matching methods.
The gradient method is a technique that utilizes the fact that the brightness gradient is constant in a minute section in the image. For example, for example, an image having a brightness change similar to the brightness change at a certain point in an image at a certain point should be compared with the image at that certain point (for example, an image at a point immediately after the certain point) Etc.), the motion vector of the certain part is calculated. The field of motion vectors of the entire image is also called optical flow.
In the block matching method, an image is divided into rectangular blocks, for example, square blocks, and motion vectors can be calculated on a rectangular block basis. For example, an image at a certain point in time is divided into rectangular blocks, and for each block, the image to be compared with the image at a certain point in time (eg, an image at a point in time immediately before the certain point in time) is best By detecting similar blocks, the motion vector of each block is calculated.

In the present technology, a pixel block is a set of pixels that constitute an image. The pixel block can be appropriately selected according to, for example, a method for calculating a motion vector. For example, the pixel block has one side of, for example, 1 to 50 pixels, particularly 2 to 30 pixels, more particularly 3 to 20 pixels, and the other side of 1 to 50 pixels, especially 2 to 30 pixels, In particular, it may be a rectangular pixel block of 3 to 20 pixels. The size of the pixel block may be appropriately set by those skilled in the art according to, for example, the method of calculating the amount of movement, the size of the image, and the size of particles flowing in the flow path. For example, when the block matching method is used to calculate the motion amount, the pixel block is, for example, 16 × 16, 8 × 16, 16 × 8, 8 × 8, 4 × 8, 8 × 4, or 4 × 4 pixels. sell. Also, if block matching is used in the calculation of the amount of motion, the pixel block may preferably be square.

In the present technology, the feature amount may be an amount representing the feature of the motion amount in the pixel block. Depending on the feature amount, for example, the velocity and / or direction of flow in the pixel block, the moving velocity of particles present in the pixel block, the tendency or average of the velocity, the direction in which particles present in the pixel block, and / or Trends in that direction can be expressed.
The feature amount may be, for example, an integrated value or an average value of motion amounts, or a maximum value of the motion amounts. These values are suitable for characterizing the amount of motion in the pixel block. For example, the integrated value, the average value, and the maximum value may be an integrated value, an average value, and a maximum value of motion vectors, respectively. The integrated value, average value, and maximum value of motion vectors can be calculated by a method known to those skilled in the art. In addition, when calculating these values, data processing, such as exclusion of an outlier, may be performed, for example.
For example, the feature amount may be, for example, a maximum value of a number representing a motion amount for each pixel block (for example, a size of a motion vector). The magnitudes of motion vectors between images are quantified for each pixel block, and the magnitudes of quantified motion vectors for each pixel block are collected over a predetermined time. The quantified maximum value of the collected motion vector may be used as the feature amount of each pixel block.
The number of data of movement amount used to obtain the feature amount is preferably 5 or more, more preferably 10 or more, more preferably 20 or more, still more preferably 100 or more, 200 or more, 500 or more, or 1000 or more It can be. A more appropriate feature amount can be obtained by the large number of motion amount data used to obtain the feature amount.

In the present technology, the flow path internal information may be, for example, information on the structure of the flow path and information on the flow in the flow path. Examples of the information on the structure of the flow path include the shape of the flow path and the area of the flow path. Information on the flow in the flow path can include, for example, the flow velocity in the flow path, the flow velocity distribution in the flow path, and the direction of the flow.

The flow channel internal information acquisition unit of the image processing apparatus according to the present technology may include a color information addition unit. The color information adding unit adds color information to each pixel block according to the feature amount. The color information may be, for example, color information based on an XYZ color system or an RGB color system. The rules regarding the application of color information may be set as appropriate by those skilled in the art. For example, the following may be set as the rule.
For example, when the feature amount of a pixel block is equal to or more than a predetermined value, the color information adding unit adds predetermined color information to all the pixels constituting the pixel block, and the color information adding unit When the feature amount is less than the predetermined value, color information different from the color information may be provided to all the pixels forming the pixel block. By providing two different color information in this manner, for example, the inside of the flow path and the other portion can be distinguished.
Also, three or more different color information may be provided depending on the feature amount. The number of types of color information to be provided may be, for example, 3 to 50, 4 to 40, 5 to 30, or 10 to 20. For example, by providing three different color information, the inside of the flow path can be distinguished from the other portions, and the inside of the flow path can be divided into a slow flow portion and a fast flow portion. Thereby, it is possible to identify the stagnation portion and / or the flow velocity reduction portion in the flow channel.
Further, the color information to be provided may be gradually changed according to the feature amount. For example, by gradually changing the X, Y, and X values in the XYZ color system or the R, G, and B values in the RGB color system according to the height of the feature amount, gradation appears in the flow path Color information may be provided. This makes it easy to grasp the flow velocity distribution in the flow channel.

The flow channel internal information acquisition unit of the image processing apparatus according to the present technology may include a flow channel area image generation unit. According to one embodiment of the present technology, the flow passage internal information acquisition unit may include a color information addition unit and a flow passage area image generation unit. The flow channel area image creation unit creates an image based on the color information of each pixel block provided by the color information application unit. By performing color reproduction based on the color information given to each pixel, an image of the flow passage area can be created.

The flow channel internal information acquisition unit of the image processing apparatus according to the present technology may include a flow velocity acquisition unit. According to one embodiment of the present technology, the flow passage internal information acquisition unit may include a flow velocity acquisition unit, a color information addition unit, and a flow passage area image generation unit. The flow velocity acquisition unit acquires the flow velocity based on the feature amount. The color information adding unit may add color information to each pixel block according to the acquired flow velocity.
The application of color information may be performed, for example, in the same manner as the rule described above, that is, color information may be applied according to a rule in which “feature amount” is replaced with “flow velocity” in the rule described above. In addition, the color information adding unit may further add other color information to the pixels forming the boundary between the slow flow rate part and the fast flow rate part. Thereby, for example, stagnant parts and / or flow rate reduction parts in the flow path can be more clearly presented to the user of the image processing apparatus of the present technology.

The flow channel internal information acquisition unit of the image processing apparatus according to the present technology may include an abnormal area identification unit. According to one embodiment of the present technology, the flow channel internal information acquisition unit may include a flow velocity acquisition unit, an abnormal area identification unit, a color information application unit, and a flow channel area image creation unit. The abnormal area identification unit can identify an abnormal area of the flow based on, for example, the flow velocity. For example, the abnormal area identification unit may generate information for indicating to the user a pixel block identified as an abnormal area as an abnormal area. Examples of the information include color information for indicating the boundary between the abnormal area and the other area, or information for causing the abnormal area to blink and display it. For example, the abnormal region identification unit can identify a portion where the flow velocity is lower than a predetermined value as a portion where thrombus is likely to occur or a portion where thrombus is occurring. The portion identified as the abnormal area may be given color information so as to give a predetermined color or pattern, for example, by the color information giving unit. Based on these pieces of information, the flow passage area image generation unit may generate a flow passage area image, or an output unit may perform an output.

The flow channel internal information acquisition unit of the image processing apparatus according to the present technology may include an area calculation unit. According to one embodiment of the present technology, the flow passage internal information acquisition unit may include an area calculation unit, a color information addition unit, and a flow passage area image creation unit.
In one embodiment, the area calculation unit can calculate the area of a pixel block in which the feature value is within a predetermined range. For example, the area of the flow path can be calculated by calculating the area of a pixel block having a feature value equal to or greater than a predetermined value. In addition, by calculating the area of the pixel block having the feature amount within the predetermined range, the area of the flow path portion which is flowing normally and / or the area of the abnormal area (for example, the area where the flow velocity decrease occurs) is calculated. It can be done.
In another embodiment, the area calculation unit may calculate the area of a pixel having predetermined color information. That is, after the color information is given by the color information giving unit, the area can be calculated based on the color information.

The flow channel internal information acquisition unit of the image processing apparatus according to the present technology may include a flow direction identification unit. According to one embodiment of the present technology, the flow passage internal information acquisition unit may include a flow direction identification unit, a color information addition unit, and a flow passage area image creation unit.
The flow direction identification unit identifies the flow direction in the pixel block based on the feature amount. By identifying the flow direction, it is possible to identify, for example, a region where swirling or turbulent flow is occurring.
The flow direction identification unit can, for example, identify only the flow direction of a pixel block having a feature amount equal to or greater than a predetermined value. As a result, the flow direction is identified only in the inside of the flow path, and the flow direction is not identified in parts other than the flow path.
The identified flow direction may be displayed, for example, as a line or an arrow in the flow path. As a result, the user of the image processing apparatus according to the present technology can more easily grasp the flow direction in the flow path.

In the present technology, the flow channel internal information acquisition unit includes one selected from a color information addition unit, a flow channel region image generation unit, a flow velocity acquisition unit, an abnormal region identification unit, an area calculation unit, and a flow direction identification unit. Two, three, four or five components may be included. Of course, in the present technology, all of these six components may be included in the internal flow channel information acquisition unit.

(2) First Example of First Embodiment (Image Processing Device)

Hereinafter, an example of an image processing apparatus according to the present technology and an example of image processing using the image processing apparatus will be described with reference to FIGS. 1 and 2. FIG. 1 is a diagram showing an example of an image processing system including an image processing apparatus according to the present technology. FIG. 2 is an example of a block diagram of an image processing apparatus according to the present technology.

As shown in FIG. 1, the image processing system 100 includes an imaging device 101 and an image processing device 200. The imaging device 101 can capture an image of a flow passage region to be imaged, and can be appropriately selected or configured by those skilled in the art. The imaging device 101 may comprise, for example, a camera, in particular a digital video camera. The imaging device 101 of FIG. 1 includes a microscope 102 and a digital video camera 103. The microscope 102 is configured to be able to observe the sample 104 including the flow passage area to be imaged. The image processing apparatus 200 is connected to the imaging apparatus 101 by wire or wireless so that the image data captured by the imaging apparatus 101 can be acquired.

As illustrated in FIG. 2, the image processing apparatus 200 includes an image recording unit 201, a motion amount calculation unit 202, a feature amount acquisition unit 203, a flow path internal information acquisition unit 204, and an output unit 207. The flow channel internal information acquisition unit 204 includes a color information addition unit 205, a flow channel area image generation unit 206, an area calculation unit 208, and a flow direction identification unit 209.

The imaging apparatus 101 captures an image of the flow passage area of the sample 104 with the digital video camera 103 via the microscope 102. The captured moving image data is stored in the image recording unit 201 in the image processing apparatus 200. An example of video data is shown in FIG. As shown in FIG. 3, the moving image data is composed of a total number t of image data from the first image data 301-1 to the t-th image data 301-t. The first image data 301-1 is an image recorded first in the imaging period of the moving image data, and the t-th image data 301-t is recorded last in the imaging period of the moving image data It is an image.

The motion amount calculation unit 202 calculates the motion amount between the images from the plurality of images constituting the moving image data. The motion amount calculation unit 202 calculates, for example, a motion vector as the motion amount. The motion amount calculator 202 calculates the amount of motion between the image data 301-1 and the image data 301-2, the amount of motion between the image data 301-2 and the image data 301-3,. The amount of movement between (t-1) and the image data 301-t is calculated.
The motion amount calculation unit 202 can compare the image data 301-1 and the image data 301-2 by, for example, the gradient method. The motion amount calculation unit 202 detects, in the image data 301-2, a portion having a luminance change similar to the luminance change at a certain portion in the image data 301-1. The motion amount calculation unit 202 calculates a motion vector from the positions of the two parts. Similarly, the motion amount calculation unit 202 calculates a motion vector between the image data 301-2 and the image data 301-3,..., Between the image data 301-(t−1) and the image data 301-t. Calculate motion vector. When the calculated motion vector is represented, for example, by an arrow, it can be represented as, for example, 302-1, 302-2, ..., and 302- (t-1) in FIG. In the present technology, the motion amount does not have to be represented by an arrow as in 302-1, 302-2, ..., and 302- (t-1) in FIG. 3, and is recorded only as data. You only need to

The feature amount acquisition unit 203 acquires, for each of the pixel blocks constituting the image, a feature amount related to the motion amount calculated by the motion amount calculation unit 202.
The imaged flow path region is divided into M pieces in the horizontal direction and N pieces in the vertical direction, for example, as shown in FIG. That is, the flow passage area is divided into pixel blocks divided into M × N grids. The feature amount acquisition unit 203 integrates or averages the motion amount calculated by the motion amount calculation unit 202 for each pixel block, or acquires the maximum value of the motion amount for each pixel block.

The internal flow channel information acquisition unit 204 acquires internal flow channel information of the flow channel region based on the feature amount acquired by the feature amount acquisition unit 203. An example in which the feature amount is an integrated value and the flow path internal information is an image of a flow path region will be described below.

The color information application unit 205 applies color information to each pixel block in accordance with the integrated value acquired by the feature amount acquisition unit 203. For example, when the integrated value of a pixel block is higher than a predetermined threshold, gray color information is added to the pixel block, and when the integrated value of a pixel block is lower than the threshold, the pixel The block is given white color information.

The flow path area image creation unit 206 creates a flow path area image from the color information provided to each pixel block by the color information addition unit 205. For example, a flow path area image as shown on the right side of FIG. 5 is created. The image shown on the left side of FIG. 5 is a diagram showing each pixel block by a grid. The shape of the flow path can be grasped by the flow path region image.

The area calculation unit 208 calculates, for example, the area of a pixel block to which gray color information is added by the color information addition unit 205. Thereby, the area of the flow path in the created image can be calculated.

The flow direction identification unit 209 identifies the flow direction in the pixel block based on, for example, the feature amount. In the identification, for example, the flow direction in the pixel block designated by the user can be identified.

The output unit 207 outputs the flow channel area image generated by the flow channel area image generation unit 206. The output unit 207 may be, for example, a display or a printer.
Further, the output unit 207 may output the area calculated by the area calculation unit 208 simultaneously with the output of the flow path region image.
In addition, the output unit 207 may display the flow direction identified by the flow direction identification unit 209, for example, by an arrow or a line on the flow passage area image.

In addition, by applying the image processing according to the present technology, for example, at two different times, it is also possible to observe the generation and disappearance of the flow path. For example, by performing image processing according to the present technology based on a moving image obtained by imaging a certain flow channel area at a certain time, an image shown in the left of FIG. 11 is obtained. Next, after a predetermined time has elapsed from the time, for example, after 24 hours, the image processing shown in the right of FIG. 11 is performed by performing the image processing according to the present technology based on the moving image obtained by imaging the flow channel area. obtain. By comparing the images shown on the left and right of FIG. 11, the generation of the flow channel can be observed. This can, for example, assess angiogenesis and axonal generation.

(3) Second example of the first embodiment (image processing apparatus)

Hereinafter, an example of an image processing apparatus according to the present technology and an example of image processing using the image processing apparatus will be described with reference to FIGS. 1 and 6. FIG. 1 is as described above. FIG. 6 is an example of a block diagram of an image processing apparatus according to the present technology.

As illustrated in FIG. 6, the image processing apparatus 200 includes an image recording unit 601, a motion amount calculation unit 602, a feature amount acquisition unit 603, a flow channel internal information acquisition unit 604, and an output unit 607. The flow channel internal information acquisition unit 604 includes a color information addition unit 605, a flow channel area image generation unit 606, a flow velocity acquisition unit 608, and an abnormal area identification unit 609.

The imaging apparatus 101 captures an image of the flow passage area of the sample 104 with the digital video camera 103 via the microscope 102. The captured moving image data is stored in the image recording unit 601 in the image processing apparatus 200. An example of the video data is shown in FIG. As shown in FIG. 7, moving image data is composed of a total number t of image data from the first image data 701-1 to the t-th image data 701-t. The first image data 701-1 is an image recorded first in the imaging period of the moving image data, and the t-th image data 701-t is recorded last in the imaging period of the moving image data It may be an image.

The motion amount calculation unit 602 calculates a motion amount between images from a plurality of images forming the moving image data. The motion amount calculation unit 602 calculates a motion vector as the motion amount. The motion amount calculation unit 602 calculates the amount of motion between the image data 701-1 and the image data 701-2, the amount of motion between the image data 701-2 and the image data 701-3,. The amount of movement between (t-1) and the image data 701-t is calculated. The calculation of the movement amount will be described below.
For example, the motion amount calculation unit 602 can compare the image data 701-1 and the image data 701-2 by the block matching method. The motion amount calculation unit 602 divides, for example, each of the image data 701-1 and the image data 701-2 into M × N pixel blocks shown in FIG. That is, the motion amount calculation unit 602 divides both image data into M pieces in the horizontal direction and into N pieces in the vertical direction. The pixel block may be, for example, a square block of 16 × 16 pixels. The motion amount calculation unit 602 detects a block closest to a certain block in the image data 701-1 in the image data 701-2. The motion amount calculation unit 602 calculates a motion vector from the positions of the two blocks.
Similarly, the motion amount calculation unit 602 calculates a motion vector between the image data 701-2 and the image data 701-3, ..., and the image data 701- (t-1) and the image data 701-t. Calculate the motion vector between them. When the calculated motion vector is represented by an arrow, it can be represented as, for example, 702-1, 702-2, ..., and 702- (t-1) in FIG.
Some of the arrows indicated by 702-1, 702-2, ..., and 702- (t-1) in FIG. 7 are smaller than the other arrows. This small arrow schematically indicates that the motion vector is smaller than the others. In the present technology, the motion amount does not have to be represented by an arrow as in 702-1, 702-2, ..., and 702- (t-1) in FIG. 7, and is recorded only as data. May be

The feature amount acquisition unit 603 acquires a feature amount related to the motion amount calculated by the motion amount calculation unit 602 for each of the pixel blocks constituting the image. For example, the feature amount acquiring unit 603 integrates or averages the motion amounts calculated by the motion amount calculating unit 602 for each of the M × N pixel blocks shown in FIG. 4 described above, or The maximum value of the motion amount is acquired for each block.

The flow channel internal information acquisition unit 604 acquires flow channel internal information of the flow channel region based on the feature amount acquired by the feature amount acquisition unit 603. An example in which the feature amount is an average value and the flow channel internal information is a flow velocity in the flow channel will be described below.

The flow velocity acquisition unit 608 calculates the flow velocity of each pixel block from the average value acquired by the feature amount acquisition unit 603. Based on the calculated flow velocity, for example, the color information giving unit 605 described above gives color information to the pixel block.
The color information addition unit 605, for example, applies the first color information to the pixel block whose flow rate is equal to or higher than the first threshold. The color information addition unit 605 applies second color information to pixel blocks that are equal to or greater than a second threshold that is less than the first threshold and that is lower than the first threshold. The color information adding unit 605 adds third color information to the pixel block smaller than the second threshold. Thus, for example, the pixel block provided with the first color information has a high flow velocity, and the pixel block provided with the second color information has a low flow velocity, and the pixel block provided with the third color information has It can be shown that there is no flow rate (i.e., out of the flow path).

FIGS. 7 and 8 show an example in which the color information adding unit 605 adds dark gray as the first color information, light gray as the second color information, and white to the third color information. Refer to the description.
In FIG. 7, in the pixel block for which the motion vector indicated by the small arrow is calculated, the average value is smaller than that in the other flow passage region, and as a result, the flow velocity is less than the first threshold and the second The threshold is 2 or more. As a result, light gray color information is given to the pixel block.
In FIG. 7, in the pixel block in which the motion vector indicated by the other arrow is calculated, the flow velocity is equal to or higher than the first threshold. As a result, dark gray color information is given to the pixel block.
In FIG. 7, in the pixel block in which the arrow is not shown, the motion vector is extremely small, and as a result, the flow velocity is less than the second threshold. Therefore, white color information is given to the pixel block.
A channel area image created as a result of the channel area image creation unit 606 performing color reproduction based on the color information provided by the color information application unit 605 is shown on the right of FIG. As shown on the right of FIG. 8, the portions shown in dark gray and light gray are the flow channel regions. In the flow channel area, a portion shown in light gray indicates a portion where the flow velocity is slower than that of the other flow channel areas. White parts are outside the flow path. From the image on the right of FIG. 8, the user can not only understand the shape of the flow path, but also can distinguish between the high flow velocity portion and the low flow velocity portion.
The image shown on the left side of FIG. 8 is a diagram showing each pixel block by a grid.
Also, in addition to the calculation of the flow velocity, the flow direction in each pixel block may be determined. The determination of the flow direction may be performed by, for example, the flow direction identification unit described above.

Further, an example in which the feature amount acquired by the feature amount acquisition unit 603 is the maximum value will be described below.
As described above with reference to FIG. 7, the motion amount calculation unit 602 calculates the motion vector between the image data 701-1 and the image data 701-2, the image data 701-2 and the image data 701-3. Motion vectors between the image data 701- (t-1) and the image data 701-t are calculated. The magnitudes of the calculated motion vectors are digitized for each pixel block as shown in FIG. For example, the portion surrounded by the square in the image of 702-1 is represented as 703-1. As shown in 703-1, the magnitude of the motion vector is quantified for each pixel block. Similarly, as indicated by 703-2 to 703- (t-1), the magnitude of the motion vector for each pixel block is digitized. From the grid information of each of these 703-1 to 703- (t-1), as indicated by 704, the maximum value for each pixel block is obtained. As shown in 704, for example, the portion represented by 5 has a high flow rate, but the portion represented by 0 or 1 has no or small flow rate. Therefore, in the part represented by 0 or 1, there is considered to be a factor that impedes the flow.
Also, as indicated by 703-1 to 703- (t-1) and 704, color information may be given to each pixel block according to the maximum value. The application of the color information may be performed as described above.

The abnormal area identification unit 609 identifies, for example, a pixel block to which the second color information is added as an abnormal area. For example, the abnormal area identification unit 609 can generate information for indicating the pixel block as an abnormal area to the user. Examples of the information include color information for indicating the boundary between the abnormal area and the other area, information for blinking the abnormal area for display, or information for displaying an arrow pointing to the abnormal area. . Based on these pieces of information, the flow channel area image generation unit 606 can generate a flow channel area image, or an output unit can perform output.

The output unit 607 outputs the flow channel area image created as described above. The output unit 607 may be, for example, a display or a printer.

2. Second embodiment (image processing method)

(1) Description of the second embodiment

The present technology includes a motion amount calculating step of calculating a motion amount between at least two images of a plurality of images obtained by imaging the flow passage region continuously in time, and at least an image of the flow passage region. A feature amount obtaining step of obtaining a feature amount related to the movement amount for each pixel block constituting a part; and a flow passage internal information obtaining step of obtaining flow passage internal information of the flow passage region based on the feature amount. Provide an image processing method including:

The flow path internal information can be acquired by the image processing method according to the present technology. The acquired flow channel internal information is as described in the above “1. First embodiment (image processing apparatus)”.

(2) Example of Second Embodiment (Image Processing Method)

Hereinafter, an example of an image processing method according to the present technology will be described with reference to FIGS. 1 and 2 are as described in the above "1. First embodiment (image processing apparatus)". FIG. 9 is a diagram illustrating an example of the flow of an image processing method according to the present technology.

In step S101, the image processing apparatus 200 starts image processing according to the present technology.

In step S102, the motion amount calculation unit 202 acquires image data, for example, moving image data. The image data may be recorded in the image recording unit 201, or may be recorded in the imaging device 101 (in particular, the digital video camera 103).

In step S103, the motion amount calculation unit 202 calculates the amount of motion between at least two of the plurality of images included in the acquired image data. The contents described in regard to the motion amount calculation unit in the above “1. First embodiment (image processing apparatus)” also apply to the motion amount calculation step of step S103. For example, in step S103, the motion amount calculation unit 202 calculates the amount of motion between the image data 301-1 and the image data 301-2 in FIG. 3 and the motion between the image data 301-2 and the image data 301-3. The amount,..., The amount of movement between the image data 301- (t-1) and the image data 301-t can be calculated. The motion amount may be calculated by, for example, the gradient method or the block matching method.

In step S104, the feature amount acquisition unit 203 acquires a feature amount related to the movement amount for each pixel block of the image including the flow channel area. The contents described in relation to the feature amount acquisition unit in “1. First embodiment (image processing apparatus)” also apply to the feature amount acquisition step of step S104. For example, in step S104, the feature amount acquisition unit 203 integrates or averages the motion amounts calculated by the motion amount calculation unit 202 for each pixel block, or acquires the maximum value of the motion amount for each pixel block. sell.

In step S105, the flow channel internal information acquisition unit 204 acquires flow channel internal information of the flow channel region based on the feature amount. The contents described regarding the flow path internal information acquisition unit in the above “1. First embodiment (image processing apparatus)” also apply to the flow path internal information acquisition step of step S105. For example, in step S105, the color information addition unit 205 of the flow path internal information acquisition unit 204 can add color information to each pixel block in accordance with the integrated value acquired by the feature amount acquisition unit 203. Then, in the same step, the flow channel area image generation unit 206 of the flow channel internal information acquisition unit 204 can generate a flow channel area image from the color information provided to each pixel block by the color information addition unit 205.

In step S106, the output unit 207 outputs the acquired channel internal information. For example, the flow path area image created by the flow path internal information acquisition unit 204 may be displayed by a display or printed by a printer.

In step S107, the image processing apparatus 200 ends the image processing according to the present technology.

3. Third embodiment (program for image processing)

(1) Description of the third embodiment

The present technology includes a motion amount calculating step of calculating a motion amount between at least two images of a plurality of images obtained by imaging the flow passage region continuously in time, and at least an image of the flow passage region. A feature amount obtaining step of obtaining a feature amount related to the movement amount for each pixel block constituting a part; and a flow passage internal information obtaining step of obtaining flow passage internal information of the flow passage region based on the feature amount. An image processing program to be executed by a computer is also provided.

That is, the image processing program according to the present technology is a program for causing a computer to execute the image processing method according to the present technology. Since each process performed by the said program is as having described in said "2. 2nd Embodiment (image processing method)", description about these is abbreviate | omitted.

4. Hardware configuration example

Hereinafter, an example of a hardware configuration of an information processing apparatus that realizes an image processing apparatus according to the present technology will be described with reference to FIG. FIG. 10 is a diagram showing an example of a schematic hardware configuration of an information processing apparatus for realizing the image processing apparatus according to the present technology.

An information processing apparatus 1001 illustrated in FIG. 10 includes a CPU (central processing unit) 1002 and a RAM 1003. The CPU 1002 and the RAM 1003 are connected to each other via a bus 1005, and are also connected to other components of the information processing apparatus 1001 via the bus 1005.
The CPU 1002 performs control and calculation of the information processing apparatus 1001. As the CPU 1002, any processor can be used, and examples thereof include a processor of Xeon (registered trademark) series, Core (trademark) series, or Atom (trademark) series. Each component of the image processing apparatus 200 described with reference to FIG. 2 can be realized by the CPU 1002, for example.
The RAM 1003 includes, for example, a cache memory and a main memory, and can temporarily store a program used by the CPU 1002 and the like.

The information processing apparatus 1001 may include a disk 1004, a communication device 1006, an output device 1007, an input device 1008, and a drive 1009. Any of these components can be connected to the bus 1005.
The disk 1004 includes an operating system (for example, WINDOWS (registered trademark), UNIX (registered trademark), LINUX (registered trademark), etc.), an image processing program according to the present technology and various other programs, and various data ( For example, image data can be stored.
The communication device 1006 connects the information processing device 1001 to the network 1010 by wire or wirelessly. The communication device 1006 can enable the information processing device 1001 to communicate with the imaging device via the network 1010. The type of communication device 1006 may be appropriately selected by those skilled in the art.
The output device 1007 can output the processing result of the information processing device 1001. Examples of the output device 1007 include, but are not limited to, a display device such as a display, an audio output device such as a speaker, or a printer.
The input device 1008 is a device for the user to operate the information processing apparatus 1001. Examples of the input device 1008 can include, but are not limited to, a mouse and a keyboard.
The drive 1009 can read out the information recorded on the recording medium and output the information to the RAM 1003 and / or write various data to the recording medium. The recording medium is, for example, a DVD medium, a flash memory, or an SD memory card, but is not limited thereto.

The present technology can also be configured as follows.
[1] A motion amount calculation unit that calculates a motion amount between at least two images among a plurality of images obtained by imaging the flow channel region continuously in time
A feature amount acquisition unit that acquires a feature amount related to the movement amount for each pixel block that constitutes at least a part of the image of the flow path region;
And a flow channel internal information acquisition unit that acquires flow channel internal information of the flow channel region based on the feature amount.
[2] The image processing apparatus according to [1], wherein the flow channel internal information acquisition unit includes a color information addition unit that adds color information to each pixel block according to the feature amount.
[3] The image processing apparatus according to [2], wherein the flow channel internal information acquisition unit further includes a flow channel region image generation unit that generates an image of the flow channel region based on the color information.
[4] The image processing apparatus according to any one of [1] to [3], wherein the flow channel internal information acquisition unit further includes a flow velocity acquisition unit that acquires the flow velocity inside the flow channel based on the feature amount.
[5] The image processing apparatus of [4], wherein the flow channel internal information acquisition unit further includes an abnormal area identification unit that identifies an abnormal area of the flow based on the flow velocity.
[6] The image processing apparatus according to any one of [1] to [5], wherein the flow channel internal information acquisition unit further includes an area calculation unit that calculates an area of a pixel block in which the feature value is within a predetermined range. .
[7] The image processing apparatus according to any one of [1] to [6], wherein the feature amount is acquired based on a motion amount of 5 or more.
[8] The image processing apparatus according to any one of [1] to [7], wherein the feature amount is an integrated value or an average value of the motion amounts, or a maximum value of the motion amounts.
[9] The image processing apparatus according to any one of [1] to [8], wherein the plurality of images constitute a moving image of the flow passage area.
[10] The image processing apparatus of [9], wherein the at least two images are continuous images among the images constituting the moving image.
[11] The image processing apparatus according to any one of [1] to [10], wherein the movement amount is a movement amount of particles flowing in the flow path.
[12] The image processing apparatus of [11], wherein the particles are biological particles.
[13] The image processing apparatus according to any one of [1] to [12], wherein the flow passage region includes a flow passage in a living body in which particles are flowing and / or an artificial flow passage in which particles are flowing.
[14] A motion amount calculating step of calculating a motion amount between at least two of a plurality of images obtained by imaging the flow passage region continuously in time,
A feature amount obtaining step of obtaining a feature amount related to the movement amount for each pixel block constituting at least a part of the image of the flow passage region;
And a flow channel internal information acquisition step of acquiring flow channel internal information of the flow channel region based on the feature amount.
[15] A motion amount calculating step of calculating a motion amount between at least two images among a plurality of images obtained by imaging the flow channel region continuously in time,
A feature amount obtaining step of obtaining a feature amount related to the movement amount for each pixel block constituting at least a part of the image of the flow passage region;
An image processing program for causing a computer to execute a flow channel internal information acquisition step of acquiring flow channel internal information of the flow channel region based on the feature amount.

DESCRIPTION OF SYMBOLS 100 Image processing system 101 Imaging device 102 Microscope 103 Digital video camera 104 Sample 200 Image processing apparatus

Claims

A motion amount calculation unit that calculates a motion amount between at least two of a plurality of images obtained by imaging the flow path region continuously in time;
A feature amount acquisition unit that acquires a feature amount related to the movement amount for each pixel block that constitutes at least a part of the image of the flow path region;
And a flow channel internal information acquisition unit that acquires flow channel internal information of the flow channel region based on the feature amount.
The image processing apparatus according to claim 1, wherein the flow path internal information acquisition unit includes a color information addition unit that adds color information to each pixel block according to the feature amount.
The image processing apparatus according to claim 2, wherein the flow channel internal information acquisition unit further includes a flow channel region image generation unit that generates an image of the flow channel region based on the color information.
The image processing apparatus according to claim 1, wherein the flow channel internal information acquisition unit further includes a flow velocity acquisition unit that acquires a flow velocity inside the flow channel based on the feature amount.
The image processing apparatus according to claim 4, wherein the flow channel internal information acquisition unit further includes an abnormal area identification unit that identifies an abnormal area of the flow based on the flow velocity.
The image processing apparatus according to claim 1, wherein the flow channel internal information acquisition unit further includes an area calculation unit that calculates an area of a pixel block in which the feature amount is within a predetermined range.
The image processing apparatus according to claim 1, wherein the feature amount is acquired based on a motion amount of 5 or more.
The image processing apparatus according to claim 1, wherein the feature amount is an integrated value or an average value of the motion amounts, or a maximum value of the motion amounts.
The image processing apparatus according to claim 1, wherein the plurality of images constitute a moving image of the flow passage area.
The image processing apparatus according to claim 9, wherein the at least two images are continuous images among images constituting the moving image.
The image processing apparatus according to claim 1, wherein the movement amount is a movement amount of particles flowing in the flow path.
The image processing apparatus according to claim 11, wherein the particles are biological particles.
The image processing apparatus according to claim 1, wherein the flow channel region includes a flow channel in a living body in which particles flow and / or an artificial flow channel in which particles flow.
A motion amount calculating step of calculating a motion amount between at least two of a plurality of images obtained by imaging the flow path region continuously in time;
A feature amount obtaining step of obtaining a feature amount related to the movement amount for each pixel block constituting at least a part of the image of the flow passage region;
And a flow channel internal information acquisition step of acquiring flow channel internal information of the flow channel region based on the feature amount.
A motion amount calculating step of calculating a motion amount between at least two of a plurality of images obtained by imaging the flow path region continuously in time;
A feature amount obtaining step of obtaining a feature amount related to the movement amount for each pixel block constituting at least a part of the image of the flow passage region;
An image processing program for causing a computer to execute a flow channel internal information acquisition step of acquiring flow channel internal information of the flow channel region based on the feature amount.