KR20160118037A - Apparatus and method for detecting lesion from medical image automatically - Google Patents
Apparatus and method for detecting lesion from medical image automatically Download PDFInfo
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- 230000003902 lesion Effects 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims description 45
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- 238000002591 computed tomography Methods 0.000 description 13
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- 238000010586 diagram Methods 0.000 description 5
- 238000002595 magnetic resonance imaging Methods 0.000 description 5
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Abstract
A device for automatically detecting a lesion position that automatically detects a lesion position from a medical image is disclosed. An apparatus for automatically detecting a lesion position according to the present invention includes: an input unit for receiving an original image collected through a medical device; A threshold setting unit for converting the original image into a grayscale image and then setting at least one threshold value based on a brightness value of pixels constituting the grayscale image; An image switching unit for binarizing the grayscale image into a monochrome image based on the at least one threshold value; And an outline processor for extracting contours from the binarized image by searching for components suspected to be lesions and displaying the contours on the original image.
Description
The present invention relates to an apparatus and method for automatically detecting the position of a lesion from a medical image, and more particularly, to an apparatus and method for automatically detecting a lesion position using a brightness difference in an image obtained through an endoscope.
Generally, images obtained through various medical devices are used for internal lesion screening. Examples of such images include an endoscope image, a magnetic resonance imaging (MRI), and a computed tomography (CT) image.
'Endoscope' is a medical instrument that can directly observe the internal organs or internal body cavity. It is a device designed to observe and insert a machine into organs that can not directly recognize the lesion by the naked eye without incision. Depending on the institution, there are esophagus, stomach, duodenal, etc. Depending on the method, there are fiber scope, lens system type and upper camera.
Magnetic Resonance Imaging (MRI) is a system that allows a human body to enter a large magnetic barrel that generates a magnetic field, generate high frequencies to resonate the hydrogen nuclei in the body, and measure the difference in signal from each tissue, It is a technique to image by reconfiguring through.
'Computed tomography (CT)' is a technique for capturing a human body in a circular large machine with an X-ray generator and then taking a cross-section of the human body. These computed tomography (CT) images have a merit that the structures and lesions can be seen more clearly than the simple X-ray images, and most of the organs and diseases are suspected to have lesions, It is a basic test when necessary. On the other hand, computed tomography (CT) is common to magnetic resonance imaging (MRI) in that it obtains a cross-section, but computed tomography (CT) uses X-ray and magnetic resonance imaging (MRI) There is a difference in that images are acquired by transferring.
In this way, images of magnetic resonance imaging (MRI) or computed tomography (CT) are used to detect lesions inside the body and to inform the physician of the lesion or to suspicious lesions There is a system that provides guidelines for. However, these systems do not exist at endoscopy.
On the other hand, although a technology for automatically detecting pulmonary nodules in CT has been developed, it is recognized only as a reference because its accuracy is not sufficiently high yet.
Accordingly, the present invention provides a device for automatically detecting a lesion position and a method for detecting a tissue suspected to be a lesion by using a brightness difference in a medical image, so that even minute changes that can not be visually recognized can be accurately detected.
The present invention also provides an apparatus for automatically detecting a lesion position and a method thereof for improving a cure rate by initially diagnosing a lesion.
Further, the present invention provides a device for automatically detecting a lesion position and a method thereof, which can be utilized as a tool for learning to an apprentice who is not accustomed to the diagnosis of a lesion, by providing a criterion as to whether or not the lesion- I want to.
According to an aspect of the present invention, there is provided an apparatus for automatically detecting a lesion location, including an input unit for receiving an original image collected through a medical device; A threshold setting unit for converting the original image into a grayscale image and then setting at least one threshold value based on a brightness value of pixels constituting the grayscale image; An image switching unit for binarizing the grayscale image into a monochrome image based on the at least one threshold value; And an outline processor for extracting contours from the binarized image by searching for components suspected to be lesions and displaying the contours on the original image.
Preferably, the input unit may receive an original image from an endoscope.
Preferably, the threshold value setting unit may set an average value of brightness values of all the pixels constituting the grayscale image to a first threshold value, and then, based on the first threshold value, Can be set.
Preferably, the threshold value setting unit may set the average value of brightness values of pixels constituting the grayscale image whose brightness value is larger than the first threshold value to a second threshold value.
Preferably, the threshold value setting unit may set an average value of brightness values of pixels constituting the grayscale image whose brightness value is smaller than the first threshold value to a third threshold value.
Advantageously, the image switching unit may binarize the grayscale image for each of the at least one thresholds into a monochrome image, and as a result generate the same number of monochrome images as the number of thresholds.
Preferably, the outline processor extracts the outline for each of the monochrome images, and then integrates the outlines on one original image.
Preferably, the outline processing unit may previously store a size range of the outline to be displayed on the original image, and may display only the outline within the size range.
According to another aspect of the present invention, there is provided a method for automatically detecting a lesion position, the method comprising: converting an original image collected through a medical device into a grayscale image; Setting at least one threshold value based on a brightness value of pixels constituting the grayscale image; Binarizing the grayscale image into a monochrome image based on the at least one threshold; And a contour processing step of searching for components suspected to be lesions from the binarized image, extracting contours, and displaying the contours on the original image.
Preferably, the threshold value setting step may include setting a mean value of brightness values of all the pixels constituting the grayscale image to a first threshold value; And setting at least one additional threshold based on the first threshold.
Preferably, the setting of the additional threshold value may set an average value of brightness values of pixels constituting the grayscale image whose brightness value is larger than the first threshold value to a second threshold value.
Preferably, the setting of the threshold value further comprises setting an average value of brightness values of pixels constituting the grayscale image whose brightness value is smaller than the first threshold value to a third threshold value .
Advantageously, said image conversion step binarizes said grayscale image into a monochrome image for each of said at least one thresholds, thereby producing the same number of monochrome images as said number of thresholds.
Advantageously, said contour processing step comprises the steps of: extracting said contour for each of said monochrome images; And integrating the extracted contours into the one original image.
Preferably, the contour processing step may display only an outline within a size range of a predetermined contour line.
The present invention is advantageous in that, when an endoscopic examination is performed, a boundary line for tissues suspected to be lesions is detected using a brightness difference in an image, thereby allowing a doctor to accurately detect minute changes that are hard to be visually recognized. Particularly, the present invention is advantageous in that accurate detection results can be obtained by automatically detecting binocular positions and reducing binarization errors using multiple threshold values.
Accordingly, the present invention provides a method for diagnosing lesions in an early stage to increase the cure rate of a lesion, and provides a criterion as to whether a lesion is diagnosed as an abnormal tissue, It is possible to use as. In addition, the present invention can be applied to a capsule endoscope which is adjusted in vitro at the time of remote medical examination, so that it is possible to guide the examination doctor to a tissue suspected to have a problem and to move to a suspected lesion site or to automate the movement of the capsule endoscope .
1 is a schematic block diagram of an automatic lesion position sensing apparatus according to an embodiment of the present invention.
2 is a flowchart illustrating a method of automatically detecting a lesion location according to an exemplary embodiment of the present invention.
FIG. 3 is a schematic flowchart of the process of setting the multiple thresholds in FIG.
FIG. 4 is a schematic processing flowchart of the image switching process of FIG. 2. FIG.
5 is a diagram illustrating images generated for each processing step according to an exemplary embodiment of the present invention.
FIGS. 6 and 7 are diagrams illustrating the effect of the present invention.
While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the invention is not intended to be limited to the particular embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. Like reference numerals are used for like elements in describing each drawing.
The terms first, second, A, B, etc. may be used to describe various elements, but the elements should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. And / or < / RTI > includes any combination of a plurality of related listed items or any of a plurality of related listed items.
It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.
The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises" or "having" and the like are used to specify that there is a feature, a number, a step, an operation, an element, a component or a combination thereof described in the specification, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.
Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as either ideal or overly formal in the sense of the present application Do not.
Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Throughout the specification and claims, where a section includes a constituent, it does not exclude other elements unless specifically stated otherwise, but may include other elements.
1 is a schematic block diagram of an automatic lesion position sensing apparatus according to an embodiment of the present invention. 1, an
The
The threshold
First, the threshold
At this time,
Also, the threshold
Therefore, in order to reduce such an error, the threshold
At this time,
At this time,
The
For example, when binarization is performed based on the first threshold value generated by the threshold
The
The
Here, the component refers to a white inside area at points where black and white meet in the binarized image, and the
When one or more monochrome images are generated in the
In this case, the method of extracting the contour line sequentially scans the brightness values of the pixels constituting the image, and when the brightness value is compared with neighboring pixels, the corresponding pixel is displayed, Can be used.
At this time, the method of extracting the contour line by the above method is only one embodiment, and the contour line extraction method of the present invention is not limited to the above-described method. That is, various known techniques may be applied to the method of extracting the contours of the components.
On the other hand, the
At this time, the
The
2 is a flowchart illustrating a method of automatically detecting a lesion location according to an exemplary embodiment of the present invention. 1 and 2, a method for automatically detecting a lesion position according to an embodiment of the present invention is as follows.
First, in step S100, it is determined whether an original image (i.e., a medical image) collected from the medical instrument is input to the
In step S200, the threshold
In step S300, the
In step S400, the
In step S500, the
In step S600, the
FIG. 3 is a schematic flowchart of the process of setting the multiple thresholds in FIG. Referring to FIGS. 1 and 3, the multi-threshold setting process (S300) of FIG. 2 is as follows.
First, in step S310, the threshold
In step S320, the threshold
In step S330, the threshold
In step S340, the threshold
For this, the threshold
Here, in the example of FIG. 3, an example of a process of deriving the first to third thresholds is shown, but the present invention is limited to deriving the first to third thresholds It is not. For example, it is possible to derive only the first and second threshold values and to process subsequent processes based on the values. However, when the threshold value exceeds 3, there arises a problem that the efficiency is inferior in terms of operation speed and accuracy. Therefore, it is preferable to set the maximum threshold value to three.
FIG. 4 is a schematic processing flowchart of the image switching process of FIG. 2. FIG. Referring to FIGS. 1 and 4, the image switching process (S400) of FIG. 2 is as follows.
First, in step S410, the
In step S420, the
In step S430, the
That is, the image conversion process (S400) binarizes the gray-scale image into a monochrome image for each of the at least one threshold value, thereby generating the same number of monochrome images as the number of thresholds.
FIGS. 6 and 7 are diagrams for illustrating the effect of the present invention, and are views for explaining the results of performance evaluation by applying the present invention step by step.
6 (a) shows an example of a case in which a physician examines a lesion area A on an endoscopic image, FIG. 6 (b) shows a first threshold Threshold, FIG. 6C shows an example of binarization based on the first threshold value, and then only the derived outline is displayed on the original image. FIG. 6C shows an example of binarization based on the first threshold value, And an additional generated second threshold value (
Referring to Fig. 6B, the lesion area A indicated by the physician in charge of screening is not shown as an outline in Fig. 6A. This is due to the error caused when the camera threshold is set to the average brightness value while the camera illumination is concentrated in the bottom right corner of the original image.
Referring to Fig. 6 (c), an outline B similar to the lesion area A indicated by the physician in charge of physical examination is shown in Fig. 6 (a). This is because the second threshold value derived by Equation (2)
An example of numerical representation of this effect is illustrated in FIG. 7, the first stage (1 st stage) is a stage when only the average threshold value (i.e., the first threshold value) is applied, the second stage (2 nd stage)
The result produced in the first stage (1 st stage) of FIG. 7 is shown in FIG. 6 (b). Referring to FIG. 6 (b) and FIG. 7 together, the boundary lines shown in FIG. 6 (b) are actually suspected lesions due to the difference in brightness with the surroundings even though there is no lesion. This is defined as a 'false positive' because the voice (normal) is judged to be positive (lesion). On the other hand, since the actual lesion site can not be detected as a lesion, this part is 'false negative' in which the lesion is judged as negative (normal).
On the other hand, as a result of the second step (2 nd stage) of Figure 7 is illustrated in (c) of FIG. Referring to FIG. 6 (c) and FIG. 7 together, at this stage, the portion of the lesion suspected to be a lesion, that is, 'false positive' increased to eight. The true lesion is detected as a lesion and is defined as 'true positive'. Accordingly, 'false negative' was removed.
Thus, by applying multiple thresholds, the present invention can detect and display all suspicious lesions, thereby reducing the probability of missed lesions by guiding the surgeon to a physician or an unskilled trainee. That is, it is a top priority of the present invention to remove a 'false negative' that is not recognized as a lesion even though it is a lesion. Referring to FIGS. 6 and 7, it can be confirmed that the target is achieved through setting multiple threshold values.
The above-described embodiments of the present invention can be embodied in a general-purpose digital computer that can be embodied as a program that can be executed by a computer and operates the program using a computer-readable recording medium.
The computer readable recording medium includes a magnetic storage medium (e.g., ROM, floppy disk, hard disk, etc.), optical reading medium (e.g., CD ROM, DVD, etc.).
The present invention has been described with reference to the preferred embodiments.
It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is defined by the appended claims rather than by the foregoing description, and all differences within the scope of equivalents thereof should be construed as being included in the present invention.
Claims (15)
A threshold setting unit for converting the original image into a grayscale image and then setting at least one threshold value based on a brightness value of pixels constituting the grayscale image;
An image switching unit for binarizing the grayscale image into a monochrome image based on the at least one threshold value; And
And a contour processing unit for searching for components suspected to be lesions from the binarized image, extracting contours, and displaying the contours on the original image.
And an original image is received from the endoscope.
Further comprising setting at least one additional threshold value based on the first threshold value after setting an average value of brightness values for all the pixels constituting the gray level image as a first threshold value, Sensing device.
And sets an average value of brightness values of pixels constituting the grayscale image whose brightness value is larger than the first threshold value as a second threshold value.
And sets an average value of brightness values of pixels constituting the gray-scale image whose brightness value is smaller than the first threshold value as a third threshold value.
And binarizes the gray-scale image into a monochrome image for each of the at least one threshold value, thereby generating the same number of monochrome images as the number of thresholds.
Extracting the contour line for each of the black and white images, and then displaying the contours on a single original image.
Wherein a size range of an outline to be displayed on the original image is previously stored and only an outline within the size range is displayed.
Setting at least one threshold value based on a brightness value of pixels constituting the grayscale image;
Binarizing the grayscale image into a monochrome image based on the at least one threshold; And
And a contour processing step of searching for components suspected to be lesions from the binarized image, extracting contours, and displaying the contours on the original image.
Setting an average value of brightness values of all the pixels constituting the grayscale image to a first threshold value; And
Further comprising setting at least one additional threshold based on the first threshold value.
Wherein the average value of brightness values of pixels constituting the grayscale image whose brightness value is greater than the first threshold value is set as a second threshold value.
Further comprising setting an average value of brightness values of pixels constituting the gray-scale image whose brightness value is smaller than the first threshold value to a third threshold value .
Binarizing the gray-scale image into a monochrome image for each of the at least one threshold value, and thereby generating the same number of monochrome images as the number of thresholds.
Extracting the contour for each of the monochrome images; And
And integrating the extracted contours into the one original image. ≪ RTI ID = 0.0 > 8. < / RTI >
Wherein only an outline within a size range of a predetermined outline is displayed.
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| KR1020150046279A KR20160118037A (en) | 2015-04-01 | 2015-04-01 | Apparatus and method for detecting lesion from medical image automatically |
| PCT/KR2016/003432 WO2016159726A1 (en) | 2015-04-01 | 2016-04-01 | Device for automatically sensing lesion location from medical image and method therefor |
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| KR1020150046279A KR20160118037A (en) | 2015-04-01 | 2015-04-01 | Apparatus and method for detecting lesion from medical image automatically |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190117187A (en) * | 2018-04-06 | 2019-10-16 | 주식회사 뷰노 | Method for visualizing medical image and apparatus using the same |
| US11741598B2 (en) | 2019-05-14 | 2023-08-29 | Vuno, Inc. | Method for aiding visualization of lesions in medical imagery and apparatus using the same |
| KR102613718B1 (en) | 2022-09-06 | 2023-12-14 | 주식회사 에어스메디컬 | Method, program, and apparatus for object tracking based on medical imaging |
| KR20240034428A (en) | 2022-09-07 | 2024-03-14 | (주)임팩티브에이아이 | Method, program and apparatus for developing new products based on artificial intelligence |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109346159B (en) * | 2018-11-13 | 2024-02-13 | 平安科技（深圳）有限公司 | Case image classification method, device, computer equipment and storage medium |
| CN113723417B (en) * | 2021-08-31 | 2024-04-12 | 深圳平安智慧医健科技有限公司 | Single view-based image matching method, device, equipment and storage medium |
| CN114903408A (en) * | 2022-04-22 | 2022-08-16 | 华伦医疗用品(深圳)有限公司 | Endoscope imaging system with diagnostic imaging |
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| JP4298258B2 (en) * | 2002-10-11 | 2009-07-15 | 株式会社日立メディコ | Medical image display device |
| KR20140033332A (en) * | 2010-12-17 | 2014-03-18 | 오르후스 우니베르시테트 | Method for delineation of tissue lesions |
| KR101223598B1 (en) * | 2011-04-06 | 2013-01-22 | 인하대학교 산학협력단 | A System for Diagnosing Pancreatic Intraepithelial Neoplasia |
| KR101795720B1 (en) * | 2011-05-12 | 2017-11-09 | 주식회사 미래컴퍼니 | Control method of surgical robot system, recording medium thereof, and surgical robot system |
| KR101492254B1 (en) * | 2013-05-14 | 2015-02-10 | 사회복지법인 삼성생명공익재단 | Ultrasound diagnostic apparatus and method for quality control |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190117187A (en) * | 2018-04-06 | 2019-10-16 | 주식회사 뷰노 | Method for visualizing medical image and apparatus using the same |
| US11741598B2 (en) | 2019-05-14 | 2023-08-29 | Vuno, Inc. | Method for aiding visualization of lesions in medical imagery and apparatus using the same |
| KR102613718B1 (en) | 2022-09-06 | 2023-12-14 | 주식회사 에어스메디컬 | Method, program, and apparatus for object tracking based on medical imaging |
| KR20240034428A (en) | 2022-09-07 | 2024-03-14 | (주)임팩티브에이아이 | Method, program and apparatus for developing new products based on artificial intelligence |
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