WO2010035518A1 - Appareil de traitement d'image médicale et programme - Google Patents

Appareil de traitement d'image médicale et programme Download PDF

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Publication number
WO2010035518A1
WO2010035518A1 PCT/JP2009/053854 JP2009053854W WO2010035518A1 WO 2010035518 A1 WO2010035518 A1 WO 2010035518A1 JP 2009053854 W JP2009053854 W JP 2009053854W WO 2010035518 A1 WO2010035518 A1 WO 2010035518A1
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similarity
region
candidate
lesion
medical image
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PCT/JP2009/053854
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English (en)
Japanese (ja)
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聡 笠井
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コニカミノルタエムジー株式会社
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/50—Clinical applications
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20104—Interactive definition of region of interest [ROI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30061—Lung

Definitions

  • the present invention relates to a medical image processing apparatus and a program.
  • CAD Computer-Aided Diagnosis
  • An image region where an interstitial disease has occurred is generally characterized by a ground glass-like shadow on an X-ray image, specifically, a shadow that is uniformly white.
  • CAD an image region having such characteristics is detected by image analysis.
  • a feature amount analysis is performed by an artificial neural network as described in Patent Document 2 above. There is a method of detecting a lesion candidate of an interstitial disease. JP-A-6-237925 JP-A-10-171910 US Pat. No. 5,319,549
  • the shape of the image is one of the characteristics for determining whether or not the disease is a true disease, and is considered useful for detecting a lesion candidate.
  • An object of the present invention is to detect a lesion candidate using a feature amount of an image shape.
  • the region of interest is detected as a candidate lesion region.
  • Candidate detection means to Display means; Control means for displaying the detection results of the lesion candidates on the display means; A medical image processing apparatus is provided.
  • the candidate detecting means calculates a feature amount of the similarity map only for an image area in which the similarity is a certain value or more in each similarity map created for the one or a plurality of different shapes in the region of interest.
  • the medical image processing apparatus according to claim 1 to be calculated is provided.
  • the candidate detection means obtains an isolated region in which pixels having a certain range of similarities are adjacent to each other in each of the similarity maps created for the one or a plurality of different shapes, and calculates a feature amount of the isolated region
  • the medical image processing apparatus uses a feature amount of the similarity map and a feature amount of the isolated region.
  • the candidate detecting means creates a similarity map indicating the similarity to the shape corresponding to the normal tissue structure and a similarity map indicating the similarity to the shape corresponding to the lesion, respectively, and corresponds to the normal tissue structure
  • the medical image processing apparatus according to any one of claims 1 to 3, wherein a feature amount of each similarity map created for a shape to be processed and a shape corresponding to a lesion is calculated and used for the feature amount analysis. .
  • the present invention it is possible to detect a lesion candidate from a medical image using the feature amount of the shape of the image.
  • FIG. 1 shows a functional configuration of a medical image processing apparatus 1 according to the present embodiment.
  • the medical image processing apparatus 1 includes a control unit 11, an operation unit 12, a display unit 13, a communication unit 14, a storage unit 15, and a candidate detection unit 16.
  • the control unit 11 is a control unit configured to include a CPU (Central Processing Unit), a RAM (Random Access Memory), and the like.
  • the control unit 11 executes various processes in cooperation with a program stored in the storage unit 15. In the processing, the control unit 11 performs various calculations and centrally controls the operation of each unit. For example, when a detection result of a lesion candidate detected from a medical image is input from the candidate detection unit 16, the control unit 11 causes the display unit 13 to display a display screen indicating the detection result.
  • the operation unit 12 includes a keyboard, a mouse, a touch panel configured integrally with the display unit 13, etc., and generates operation signals corresponding to these operations and outputs them to the control unit 11.
  • the display unit 13 is a display unit having a display or the like, and displays medical images, detection results of lesion candidates, and the like according to display control of the control unit 11.
  • the communication unit 14 includes a communication interface and communicates with an external device connected to the network. For example, the communication unit 14 receives a medical image that is a detection target of a lesion candidate from an imaging device that captures a medical image. In addition, the communication unit 14 transmits information on detection results of lesion candidates for the medical image together with the medical image to a server that manages the medical image.
  • the storage unit 15 is composed of a memory such as a hard disk, and stores programs, parameters, and the like. In addition, the storage unit 15 stores medical images to be detected as lesion candidates.
  • the candidate detection unit 16 is candidate detection means for detecting a region of a lesion candidate included in the medical image using the medical image data. Information of the detection result is output to the control unit 11.
  • the lesion candidate detection process can be realized by storing a detection processing program in the storage unit 15 and performing software processing in cooperation with the stored program and the CPU. Alternatively, a hardware circuit for detection processing may be constructed and realized by the hardware circuit.
  • FIG. 2 is a flowchart showing processing executed by the medical image processing apparatus 1.
  • lesion candidates for interstitial diseases that are lung diseases are detected for medical images obtained by X-ray imaging of the chest.
  • the candidate detection unit 16 performs lesion candidate detection processing.
  • the candidate detection unit 16 performs image analysis of the medical image and extracts a lung field region included in the medical image (step S1).
  • the method for extracting the lung field is not particularly limited, and a known method can be applied.
  • the candidate detection unit 16 may create a density histogram, determine the image portion of the high density region corresponding to the lung field region from the shape and area of the density histogram, and extract the image portion as the lung field region.
  • the candidate detection unit 16 detects the contour of the lung field region by performing template matching using a template that defines the contour of the standard lung field region. It is also possible to extract a lung field region within the contour.
  • the candidate detection unit 16 When the lung field region is extracted, the candidate detection unit 16 performs image analysis of the medical image and extracts a rib region included in the medical image (step S2).
  • the method for extracting the rib region is not particularly limited, and a known method can be applied.
  • the candidate detection unit 16 determines a number of contour lines in the vertical direction (head-to-foot direction) in the lung field region of the X-ray image, and this contour line By applying a model function determined in advance, the contour portion of the rib region is estimated.
  • the candidate detection unit 16 determines a plurality of image regions to be processed in the estimated contour portion, obtains the gradient of each pixel in the image region and the direction corresponding to the gradient by the Sobel operator, The maximum value is set as the gradient of the image area.
  • the maximum value is set as the gradient of the image area.
  • the candidate detection unit 16 sets one or a plurality of ROIs (Region Of Interest) for a region excluding the rib region in the medical image.
  • the ROI is an image area for which a similarity map, which will be described later, is to be created. For example, as shown in FIG. 3, several m ⁇ m pixel ROIs are set in the lung field region between the ribs. What is necessary is just to set the size of ROI suitably.
  • the candidate detection unit 16 performs the following processing for any one of the set one or more ROIs.
  • the candidate detection unit 16 creates a similarity map for the ROI to be processed (step S4).
  • the similarity map is a map showing the similarity between the ROI and a specific shape, and the similarity between each pixel and the specific shape is set for each pixel in the ROI. The process for creating the similarity map will be described with reference to FIG.
  • the candidate detection unit 16 sets a specific shape J (step S41).
  • the shape J may be selected as a shape that easily causes a lesion to be detected.
  • a shape such as a circle or a line may be mentioned, but a shape such as a star or an ellipse may be selected depending on the situation.
  • the candidate detection unit 16 calculates the similarity between the set shape J and a certain pixel in the ROI (this is referred to as a target pixel) (step S42).
  • a degree of similarity for example, a cross-correlation value or Euclidean distance can be used, but any index may be used as long as it is an index indicating similarity to the shape J.
  • the cross-correlation value is obtained by the following equation as the similarity.
  • f (n) is a function indicating an image having a specific shape J
  • g (m) is a function indicating an ROI image to be processed.
  • n is a pixel of an image showing a specific shape J
  • m is a pixel of interest in the ROI to be processed.
  • the cross-correlation value obtained by the above formula has a range of ⁇ 1 to +1, and approaches ⁇ 1 as the similarity is lower and approaches +1 as the similarity is higher.
  • the candidate detection unit 16 determines whether or not the processing for calculating the similarity is completed for all the pixels in the ROI (step S43). If there is an unprocessed pixel (step S43; N), the candidate detection unit 16 sets the unprocessed pixel as a target pixel (step S44), returns to step S42, and sets the similarity for the newly set target pixel. Repeat the calculation process.
  • step S43 when the similarity is calculated for all the pixels in the ROI (step S43; Y), the candidate detection unit 16 sets the similarity calculated for each pixel for each pixel in the ROI.
  • a degree map is created (step S45).
  • the case of creating a similarity map for one shape has been described.
  • the shape is changed to the above-described one. What is necessary is just to repeat a process. Thereby, a similarity map can be created for each of a plurality of different shapes.
  • step S5 the process proceeds to step S5 shown in FIG.
  • the candidate detection unit 16 calculates the feature amount of the similarity map for only the extracted image region (step S6).
  • the feature amount of the similarity map include an average value, a contrast, a variance, a standard deviation, and a skew represented by the following formula.
  • the feature amount of contrast indicates whether the distribution of the similarity in the similarity map for the extracted image region is spread flat or biased.
  • the skew is a feature amount indicating how much the distribution shape of the similarity in the similarity map for the extracted image region is deviated from a symmetrical shape as viewed from the average.
  • i represents each pixel of the similarity map
  • i max represents a pixel to be processed at the end of the similarity map
  • i min represents a pixel to be processed first.
  • P (i) indicates the similarity corresponding to each pixel of the similarity map. The calculated feature value is used for the subsequent feature value analysis.
  • the candidate detection unit 16 obtains an image region (hereinafter referred to as an isolated region) in which pixels having a certain range of similarities are adjacent to each other in the similarity map created for the ROI to be processed, and features of the isolated region
  • the amount is calculated (step S7).
  • an image forming a certain shape such as a circular image or a linear image
  • a similar degree of similarity is calculated for each pixel of the image.
  • a relatively high similarity such as 0.8 or 0.9 is calculated for each pixel of the circular image.
  • a low similarity such as 0.3 or 0.4 is calculated. That is, it can be estimated that an isolated region where pixels having similar degrees of similarity calculated are adjacent is an image region having some shape.
  • the candidate detection unit 16 uses the number and area of image regions in which pixels with high similarity are adjacent in such a circular image, that is, the similarity map created for the circular shape, as the feature amount of the isolated region. calculate.
  • the candidate detection unit 16 creates a similarity histogram using the similarity map, and extracts pixels corresponding to the upper 5% in the histogram from the ROI pixels.
  • the candidate detection unit 16 refers to an adjacent pixel with respect to a certain pixel among the extracted pixels, and if the adjacent pixel is also an extracted pixel, it is the same as each pixel as a pixel constituting the same isolated region.
  • An identification number is assigned. This identification number is a number for identifying an isolated region, and the same identification number is assigned to pixels in the same isolated region.
  • the candidate detection unit 16 repeats the above process for the adjacent pixel. Then, when there is no extracted pixel in the adjacent pixels, the above process is newly started for all the extracted pixels except for the pixels already assigned with the identification number. In this newly started process, an identification number different from the identification number already assigned is assigned.
  • identification numbers are assigned to all the pixels extracted in this way, as shown in FIG. 6, different identification is made for each isolated region in which pixels within a certain range having a similarity of the top 5% are adjacent. A number will be assigned.
  • the candidate detection unit 16 calculates the number of isolated regions, the area of each isolated region, the average value of the area, the standard deviation, and the like as the feature amount of the isolated region. These calculated feature values are used for subsequent feature amount analysis.
  • the candidate detection unit 16 performs feature amount analysis on the feature amount calculated in steps S6 and S7, and based on the analysis result, the likelihood of lesion of the ROI, that is, the possibility that the ROI includes an image of an interstitial disease Judging.
  • a technique of feature quantity analysis for example, any technique such as LDA (Liner Discriminat Analysis), QDA (Quantitative DA), ANN (Artificial Neural Network), SVM (Support Vector Machine), AdaBoost, etc. May be applied.
  • the candidate detection unit 16 uses, as learning data, a feature amount obtained in advance for a medical image that is known to be normal (no lesion) or abnormal (with a lesion) as a learning data, A linear discriminant for discriminating the feature amount group related to is calculated. The candidate detection unit 16 determines, based on this linear discriminant, whether the feature quantity obtained for the ROI to be processed belongs to a normal feature quantity group or an abnormal feature quantity group. If the feature amount obtained for the ROI to be processed belongs to the feature amount group related to the abnormality, the candidate detection unit 16 determines that the ROI is likely to include an image of an interstitial disease, It is detected as a candidate region for a qualitative disease (step S8).
  • the candidate detection unit 16 determines whether the processing has been completed for all the set ROIs (step S9). If not completed (step S9; N), the process returns to the process of step S4, the unprocessed ROI is set as the process target, and the candidate detection unit 16 repeats the processes of steps S4 to S8.
  • the candidate detection unit 16 outputs information on detection results of lesion candidates for each ROI to the control unit 11.
  • the control unit 11 displays the detection result of the lesion candidate on the display unit 13 (step S10).
  • FIG. 7 shows a display example.
  • the control unit 11 displays a medical image as shown in FIG. 7 on the display unit 13 as the detection result of the interstitial disease lesion candidate, and indicates the position of the ROI detected as the lesion candidate region in this medical image. Displays an arrow marker. When a plurality of adjacent ROIs are detected as candidates, a marker may be displayed so as to indicate the center position of these ROIs.
  • the detection result display method is not limited to the method of indicating the position of a candidate by a marker, but may be other methods such as adding red to an ROI detected as a lesion candidate.
  • the candidate detection unit 16 uses the medical image data to calculate a similarity map between the ROI and a specific shape. Create for one shape. Further, the candidate detection unit 16 calculates the feature amount of the similarity map of each ROI created for one shape. Then, the candidate detection unit 16 detects, as a lesion candidate area, an ROI that is determined to include a lesion candidate based on an analysis result obtained by analyzing the calculated feature value.
  • the candidate detection unit 16 calculates the feature amount of the similarity map only for an image region in which the similarity is a certain value or more in the similarity map as one of the feature amounts used for the feature amount analysis.
  • feature quantities can be calculated only for image areas whose similarity is a certain value or higher and the similarity to a specific shape is high, and it is possible to accurately determine whether or not the lesion is a feature quantity analysis. Can do.
  • the candidate detection unit 16 obtains an isolated region in which pixels having a certain degree of similarity are adjacent in the similarity map as one of the feature amounts used for feature amount analysis, and calculates the feature amount of the isolated region. .
  • An isolated region having the same degree of similarity has some shape, and it is considered that the number and size of the isolated regions vary depending on the presence or absence of a lesion. Therefore, by using the feature amount of the isolated region for detection of the lesion candidate, multifaceted detection can be performed, and improvement in detection accuracy is expected.
  • the candidate detection unit 16 sets the ROI in the lung field region and excludes the rib region, the image region for creating the similarity map can be narrowed down, and the processing efficiency can be improved.
  • the said embodiment is a suitable example of this invention, and is not limited to this.
  • the similarity map may be created for a plurality of different shapes.
  • the plurality of different shapes can all be shapes commonly found in lesions.
  • the candidate detection unit 16 can create a similarity map for a circle and an ellipse, and calculate the above-described feature amount using each similarity map.
  • the plurality of different shapes may be shapes often found in normal tissue structures in addition to shapes often found in lesions.
  • Normal tissue structure refers to an anatomical structure free from lesions such as lungs, heart and blood vessels. Processing in the case of a shape often found in a normal tissue structure will be described with reference to FIG. In FIG. 8, the processing steps from extracting the lung field region and rib region from the medical image and setting the ROI in the lung field region excluding the rib region are the same as the processing shown in FIG. 2. Therefore, in FIG. 8, the same step numbers are assigned to the same processing steps as in FIG.
  • the candidate detection unit 16 After setting the ROI, the candidate detection unit 16 creates a similarity map corresponding to the lesion as shown in FIG. 8 (step T11).
  • the similarity map corresponding to a lesion refers to a similarity map created by calculating a similarity with a shape often seen in a lesion.
  • the method of creating the similarity map itself is the same as the method described with reference to FIG.
  • the candidate detection unit 16 calculates a feature amount using the created similarity map (step T21).
  • the processing for calculating the feature amount is the same as the processing in steps S5 to S7 shown in FIG. 2, and the feature amount of the similarity map and the feature amount of the isolated region are calculated.
  • the candidate detection unit 16 creates a similarity map corresponding to a normal tissue structure (step T12), and calculates a feature amount using the created similarity map (step T22).
  • the similarity map corresponding to a normal tissue structure is a similarity map created by calculating a similarity with a shape often found in a normal tissue structure.
  • the method of creating the similarity map itself is the same as the method described with reference to FIG. 4, and the specific shape for calculating the similarity may be set to a shape often found in normal tissue structures.
  • the processing for calculating the feature amount is the same as the processing in steps S5 to S7 shown in FIG. 2, and the feature amount of the similarity map and the feature amount of the isolated region are calculated.
  • the candidate detection unit 16 performs feature amount analysis on the feature amount calculated using each of the similarity map corresponding to a lesion and the similarity map corresponding to a normal tissue structure, and detects a lesion candidate (step S8).
  • the detection result is displayed on the display unit 13 by the control unit 11 (step S9).
  • the similarity with the shape often seen in the lesion increases, and therefore the similarity map corresponding to the lesion tends to have a large similarity as a whole.
  • the similarity map corresponding to the normal tissue structure tends to have a small similarity as a whole. That is, the feature amount calculated from each of the similarity map corresponding to a lesion and the similarity map of a normal tissue structure is considered to show a certain tendency depending on whether or not a lesion is included in the ROI.
  • a similarity map is created that includes not only the shape often seen in lesions but also the shape often seen in normal tissue structures, and the feature amount calculated using the similarity map is used to detect lesion candidates.
  • multifaceted detection can be performed.
  • improvement in detection accuracy can be expected.
  • lesion candidate detection an example of detecting an interstitial disease from an X-ray image obtained by photographing the chest was used, but a candidate for a lesion such as a tumor or a microcalcification cluster was detected from an X-ray image obtained by photographing a breast.
  • the present invention can be applied.
  • the present invention can also be applied to detecting lesion candidates such as aneurysms from not only X-ray images but also MRA (Magnetic Resonance Angiography) images obtained by imaging the head.
  • MRA Magnetic Resonance Angiography
  • a non-volatile memory such as a ROM and a flash memory
  • a portable recording medium such as a CD-ROM
  • a carrier wave carrier wave is also applied to the present invention as a medium for providing program data according to the present invention via a communication line.
  • It can be used in the field of image processing, and can be applied to a medical image processing apparatus that analyzes a medical image and detects a region of a lesion candidate.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Apparatus For Radiation Diagnosis (AREA)

Abstract

Selon l'invention, un candidat de lésion est déterminé sur la base de la valeur caractéristique de la forme d'une image. Un appareil de traitement d'image médicale comprend un moyen de détermination de candidat afin de créer une carte de similarité représentant la similarité entre chaque région d'intérêt déterminée dans une image médicale et une forme précise pour une ou plusieurs formes différentes à l'aide de données concernant l'image médicale (étape S4) et afin de déterminer la région d'intérêt en tant que région candidate de lésion lorsque le candidat de lésion est déterminé être inclus dans la région d'intérêt sur la base du résultat d'analyse de la valeur caractéristique de chaque carte de similarité (étape S8), un moyen d'affichage et un moyen de commande pour afficher le résultat de la détermination du candidat de lésion sur le moyen d'affichage (étape S10).
PCT/JP2009/053854 2008-09-25 2009-03-02 Appareil de traitement d'image médicale et programme WO2010035518A1 (fr)

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Cited By (3)

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CN102005057A (zh) * 2010-11-17 2011-04-06 中国科学院声学研究所 一种检测彩色图像的感兴趣区域的方法
JP2014124333A (ja) * 2012-12-26 2014-07-07 Olympus Medical Systems Corp 医用画像処理装置
CN113205477A (zh) * 2020-01-30 2021-08-03 株式会社日立制作所 医用图像处理装置以及医用图像处理方法

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US10163040B2 (en) * 2016-07-21 2018-12-25 Toshiba Medical Systems Corporation Classification method and apparatus
JP6805736B2 (ja) * 2016-11-07 2020-12-23 富士通株式会社 類似症例画像検索プログラム、類似症例画像検索装置及び類似症例画像検索方法
KR102257998B1 (ko) * 2019-02-20 2021-05-31 한양대학교 에리카산학협력단 세포 계수 장치 및 방법
JP7348469B2 (ja) * 2019-04-11 2023-09-21 富士通株式会社 異常陰影特定プログラム、異常陰影特定装置及び異常陰影特定方法

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JPH08287258A (ja) * 1995-04-19 1996-11-01 Fuji Xerox Co Ltd カラー画像認識装置
JP2006095279A (ja) * 2004-08-30 2006-04-13 Toshiba Corp 医用画像表示装置

Patent Citations (2)

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JPH08287258A (ja) * 1995-04-19 1996-11-01 Fuji Xerox Co Ltd カラー画像認識装置
JP2006095279A (ja) * 2004-08-30 2006-04-13 Toshiba Corp 医用画像表示装置

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102005057A (zh) * 2010-11-17 2011-04-06 中国科学院声学研究所 一种检测彩色图像的感兴趣区域的方法
CN102005057B (zh) * 2010-11-17 2012-07-25 中国科学院声学研究所 一种检测彩色图像的感兴趣区域的方法
JP2014124333A (ja) * 2012-12-26 2014-07-07 Olympus Medical Systems Corp 医用画像処理装置
CN113205477A (zh) * 2020-01-30 2021-08-03 株式会社日立制作所 医用图像处理装置以及医用图像处理方法
CN113205477B (zh) * 2020-01-30 2023-12-08 富士胶片医疗健康株式会社 医用图像处理装置以及医用图像处理方法

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