CN104771228A - Method and device for judging whether breast mass is benign or malignant - Google Patents

Method and device for judging whether breast mass is benign or malignant Download PDF

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CN104771228A
CN104771228A CN201510127561.2A CN201510127561A CN104771228A CN 104771228 A CN104771228 A CN 104771228A CN 201510127561 A CN201510127561 A CN 201510127561A CN 104771228 A CN104771228 A CN 104771228A
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image
mammary gland
gland tumor
lump
characteristic distance
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CN104771228B (en
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庞志勇
付欣玮
陈弟虎
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Sun Yat Sen University
National Sun Yat Sen University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/004—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4887—Locating particular structures in or on the body

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Surgery (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Veterinary Medicine (AREA)
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  • Molecular Biology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)

Abstract

The invention relates to a method and a device for judging whether a breast mass is benign or malignant. The method comprises the following steps of obtaining a plurality of first image characteristics of a to-be-detected breast mass; calculating characteristic distances between the first image characteristics and second image characteristics of sample images of the breast mass respectively; according to the number of the sample images with characteristic distances smaller than a first preset value, judging whether the breast mass is benign or malignant. According to the embodiment mode of the invention, the method and the device for judging whether the breast mass is benign or malignant are used for performing quantitative analysis on images of the to-be-detected breast mass, and extracting the mass images with characteristics similar to those of known benign or malignant masses, so that a predicted value of inquiring whether the mass is benign or malignant is provided for clinical reference.

Description

The determination methods of the good evil of a kind of mammary gland tumor and device
Technical field
The present invention relates to Medical Image Processing, particularly the determination methods of the good evil of a kind of mammary gland tumor and device.
Background technology
Along with the development of science and technology, medical imaging technology, as CT Scan (CT, Computed Tomography) and nuclear magnetic resonance (MRI, Magnetic Resonance Imaging), is developed and is popularized.This makes diagnostician directly whether there is tumor by diagnostic imaging for medical use patient and to judge that benign tumors is disliked.
But in current clinical diagnosis, for the clinical analysis of breast carcinoma iconography, mainly rely on diagnostician to the qualitative evaluation of image.This diagnostic mode is not only higher to the skill requirement of diagnostician, and very consuming time.In addition, due to the complexity of breast structure itself, and the factor such as the visually-perceptible difference of diagnostician, the diagnostic result of different doctor to same patient often there are differences.Therefore, be necessary to propose a kind of method of the good evil of mammary gland tumor being carried out to objective judgement, judge with the good evil of assist physician to mammary gland tumor.
Summary of the invention
For the deficiencies in the prior art, the object of the present invention is to provide determination methods and the device of the good evil of a kind of mammary gland tumor, be intended to solve in existing mammary gland tumor judge process and too rely on the technological deficiency of doctor to the qualitative evaluation of image.
For this reason, embodiment of the present invention provides the determination methods of the good evil of a kind of mammary gland tumor, comprising:
Obtain some first characteristics of image of mammary gland tumor image to be detected;
Characteristic distance between the second characteristics of image calculating the sample image of described first characteristics of image and mammary gland tumor respectively;
The quantity being less than the sample image of the first preset value according to described characteristic distance judges the good evil of described mammary gland tumor.
Preferably, described first characteristics of image and the second characteristics of image comprise morphological feature and/or the textural characteristics of image.
Preferably, described morphological feature comprises one or more in the entropy of lump border FRACTAL DIMENSION, lump compactness, lump burr degree, lump area and lump radius distribution.
Preferably, described textural characteristics comprise the concordance of image, image with one or more in the poor entropy of entropy and image.
Preferably, the computing formula of described characteristic distance is as follows:
D = a × MD ( M → , m i → ) + b × ED ( T → , t i → )
Wherein, D is characteristic distance, represent the morphological feature vector sum texture feature vector of inquiry lump image respectively, represent the morphological feature vector sum texture feature vector of i-th lump image in data base respectively, for manhatton distance, for euclidean distance, a and b represents the weighted value of morphological feature Distance geometry textural characteristics distance respectively.
Preferably, described weighted value a is [0,1], and weighted value b is 1-a.
Preferably, described characteristic distance is less than the quantity of the sample image of the first preset value is 3 ~ 13.
Preferably, the described quantity being less than the sample image of preset value according to described characteristic distance judges that the step of the good evil of described mammary gland tumor comprises:
If described characteristic distance is less than lump amount of images optimum in the sample image of the first preset value more than one second preset value, then judged result is then optimum; Otherwise judged result is pernicious.
Preferably, described second preset value is the half that described characteristic distance is less than the quantity of the sample image of preset value.
In addition, embodiment of the present invention additionally provides the judgment means of the good evil of a kind of mammary gland tumor, comprising:
Characteristics of image acquisition module, for obtaining some first characteristics of image of mammary gland tumor image to be detected;
Characteristic distance computing module, for calculate the sample image of described first characteristics of image and mammary gland tumor respectively the second characteristics of image between characteristic distance;
Result judge module, the quantity for the sample image being less than the first preset value according to described characteristic distance judges the good evil of described mammary gland tumor.
Compared with prior art, determination methods and the device of the good evil of a kind of mammary gland tumor that embodiment of the present invention provides carry out quantitative analysis to mammary gland tumor image to be detected, extract the lump image that the feature of known good evil is close, provide inquiry lump good evil predictive value, for clinical reference.
Accompanying drawing explanation
Fig. 1 is the flow chart of the determination methods of the good evil of a kind of mammary gland tumor that embodiment of the present invention provides;
Fig. 2 is the MRI image of the inquiry mammary gland tumor according to the embodiment of the present invention;
Fig. 3 A-3D is the MRI image of the mammary gland tumor extracted from data base in the embodiment that relates to of Fig. 2.
Fig. 4 is the MRI image inquiring about mammary gland tumor according to another embodiment of the present invention;
Fig. 5 A-5D is the MRI image of the mammary gland tumor extracted from data base in the embodiment that relates to of Fig. 4;
The structural representation of the judgment means of the good evil of a kind of mammary gland tumor that Fig. 6 embodiment of the present invention provides;
Detailed description of the invention
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described further.
It is the flow chart of the determination methods of the good evil of a kind of mammary gland tumor that embodiment of the present invention provides see Fig. 1, Fig. 1.The embodiment of the determination methods of the good evil of the mammary gland tumor shown in Fig. 1 comprises step S101-S103.
In step S101, obtain some first characteristics of image of mammary gland tumor image to be detected.
Specifically, use Medical Equipment to scan mammary gland to be measured, obtain mammary gland tumor image to be measured, then obtain the first characteristics of image of mammary gland tumor image to be measured.First characteristics of image and the second characteristics of image comprise morphological feature and/or the textural characteristics of image.Wherein, morphological feature comprises one or more in the entropy of lump border FRACTAL DIMENSION, lump compactness, lump burr degree, lump area and lump radius distribution.Textural characteristics comprise the concordance of image, image with one or more in the poor entropy of entropy and image.
In step s 102, the characteristic distance between the second characteristics of image calculating the sample image of described first characteristics of image and mammary gland tumor respectively.Wherein, sample image can be located in a data base, the lump image that the feature for known good evil is close.First characteristics of image is corresponding with the second characteristics of image, and such as the first characteristics of image is for detecting mammary gland tumor morphological image feature, then the second characteristics of image also should be the morphological feature of the sample image of mammary gland tumor.
In some embodiments, the computing formula of characteristic distance is as follows:
D = a × MD ( M → , m i → ) + b × ED ( T → , t i → )
Wherein, D is characteristic distance, represent the morphological feature vector sum texture feature vector of inquiry lump image respectively, represent the morphological feature vector sum texture feature vector of i-th lump image in data base respectively, for manhatton distance, for euclidean distance, a and b represents the weighted value of morphological feature Distance geometry textural characteristics distance respectively.Wherein, weighted value a span is [0,1], and weighted value b is 1-a.
In step s 103, the quantity being less than the sample image of the first preset value according to described characteristic distance judges the good evil of described mammary gland tumor.
Some preferred embodiment in, obtaining the quantity that characteristic distance is less than the sample image of the first preset value is 3 ~ 13.Such as can sort according to mode from small to large to the characteristic distance of sample image, choose a front 3-13 sample.If described characteristic distance is less than lump amount of images optimum in the sample image of the first preset value more than one second preset value, then judged result is then optimum; Otherwise judged result is pernicious.Some preferred embodiment in, the second preset value is the half that described characteristic distance is less than the quantity of the sample image of preset value.Such as, choose front 10 sample images and observe, find wherein have 8 sample images to be the sample image of conscience tumor, then tendentiousness can think that this breast tumor is optimum.
Attempt some detailed description of the invention below to elaborate spirit and the essence of embodiment of the present invention further.
See the MRI image that Fig. 2 and Fig. 3 A-3D, Fig. 2 are the inquiry mammary gland tumor according to the embodiment of the present invention, Fig. 3 A-3D is the MRI image of the mammary gland tumor extracted from data base in the embodiment that relates to of Fig. 2.
Fig. 2 is the MRI image of mammary gland tumor to be detected, and what its Green was irised out is lump.
The lump calculated in Fig. 2 is characterized as:
Morphological feature: FRACTAL DIMENSION=1.2067, compactness=1.1795, burr degree=0.6364, area=906, entropy=3.1219 of radius distribution.
Textural characteristics: concordance=8.0406*10-4, and entropy=8.2383, difference entropy=5.6784.
The distance of each mammary gland tumor image feature value in inquiry mammary gland tumor image feature value and data base is calculated according to range equation.
According to distance-taxis, from data base, extract the known good pernicious lump image that 4 distances are the shortest
Fig. 3 A-3D is the MRI image of 4 mammary gland tumor from data base's extraction.The Clinicopathologic Diagnosis of 4 the breast MRI images extracted is optimum.Fig. 2 is judged as optimum, and result conforms to clinical pathology result.
Fig. 4 is the MRI image of the mammary gland tumor to be detected of another embodiment, and what its Green was irised out is lump.
The lump calculated in Fig. 4 is characterized as:
Morphological feature: FRACTAL DIMENSION=1.4239, compactness=1.5626, burr degree=0.7522, area=1805, entropy=3.3219 of radius distribution.
Textural characteristics: concordance=4.0214*10-4, and entropy=8.5612, difference entropy=5.7332.
The distance of each mammary gland tumor image feature value in inquiry mammary gland tumor image feature value and data base is calculated according to range equation.
According to distance-taxis, from data base, extract the known good pernicious lump image that 4 distances are the shortest
Fig. 5 A-5D is the MRI image of 4 mammary gland tumor from data base's extraction.The Clinicopathologic Diagnosis of 4 the breast MRI images extracted is pernicious.Fig. 4 is judged as pernicious, and result conforms to clinical pathology result.
In addition, the embodiment of the present invention additionally provides the judgment means of the good evil of a kind of mammary gland tumor.
The structural representation of the judgment means of the good evil of a kind of mammary gland tumor provided see Fig. 6, Fig. 6 embodiment of the present invention.Judgment means shown in Fig. 4 comprises characteristics of image acquisition module 10, characteristic distance computing module 20 and result judge module 30.
Wherein, characteristics of image acquisition module 10 is for obtaining some first characteristics of image of mammary gland tumor image to be detected.Characteristic distance computing module 20 for calculate the sample image of described first characteristics of image and mammary gland tumor respectively the second characteristics of image between characteristic distance.Result judge module 30 judges the good evil of described mammary gland tumor for the quantity of the sample image being less than the first preset value according to described characteristic distance.
As can be seen from above-mentioned embodiment, the determination methods of the good evil of the mammary gland tumor that embodiment of the present invention provides and device carry out quantitative analysis to mammary gland tumor image to be detected, extract the lump image that the feature of known good evil is close, provide inquiry lump good evil predictive value, for clinical reference.
Should be appreciated that, the present invention is not limited to above-mentioned embodiment, every the spirit and scope of the present invention are not departed to various change of the present invention or modification, if these are changed and modification belongs within claim of the present invention and equivalent technologies scope, then the present invention also means that comprising these changes and modification.

Claims (10)

1. a determination methods for the good evil of mammary gland tumor, is characterized in that it comprises:
Obtain some first characteristics of image of mammary gland tumor image to be detected;
Characteristic distance between the second characteristics of image calculating the sample image of described first characteristics of image and mammary gland tumor respectively;
The quantity being less than the sample image of the first preset value according to described characteristic distance judges the good evil of described mammary gland tumor.
2. the determination methods of the good evil of a kind of mammary gland tumor as claimed in claim 1, is characterized in that: described first characteristics of image and the second characteristics of image comprise morphological feature and/or the textural characteristics of image.
3. the determination methods of the good evil of a kind of mammary gland tumor as claimed in claim 2, is characterized in that: described morphological feature comprise in the entropy of lump border FRACTAL DIMENSION, lump compactness, lump burr degree, lump area and lump radius distribution one or more.
4. the determination methods of the good evil of a kind of mammary gland tumor as claimed in claim 2, is characterized in that: described textural characteristics comprise the concordance of image, image with one or more in the poor entropy of entropy and image.
5. the determination methods of the good evil of a kind of mammary gland tumor as claimed in claim 1, is characterized in that: the computing formula of described characteristic distance is as follows:
D = a × MD ( M → , m i → ) + b × ED ( T → , t i → )
Wherein, D is characteristic distance, represent the morphological feature vector sum texture feature vector of inquiry lump image respectively, represent the morphological feature vector sum texture feature vector of i-th lump image in data base respectively, for manhatton distance, for euclidean distance, a and b represents the weighted value of morphological feature Distance geometry textural characteristics distance respectively.
6. the determination methods of the good evil of a kind of mammary gland tumor as claimed in claim 5, is characterized in that: described weighted value a is [0,1], and weighted value b is 1-a.
7. the determination methods of the good evil of a kind of mammary gland tumor as claimed in claim 1, is characterized in that: the quantity that described characteristic distance is less than the sample image of the first preset value is 3 ~ 13.
8. the determination methods of the good evil of a kind of mammary gland tumor as claimed in claim 1, is characterized in that: the described quantity being less than the sample image of preset value according to described characteristic distance judges that the step of the good evil of described mammary gland tumor comprises:
If described characteristic distance is less than lump amount of images optimum in the sample image of the first preset value more than one second preset value, then judged result is then optimum; Otherwise judged result is pernicious.
9. the determination methods of the good evil of a kind of mammary gland tumor as claimed in claim 8, is characterized in that: described second preset value is the half that described characteristic distance is less than the quantity of the sample image of preset value.
10. a judgment means for the good evil of mammary gland tumor, is characterized in that it comprises:
Characteristics of image acquisition module, for obtaining some first characteristics of image of mammary gland tumor image to be detected;
Characteristic distance computing module, for calculate the sample image of described first characteristics of image and mammary gland tumor respectively the second characteristics of image between characteristic distance;
Result judge module, the quantity for the sample image being less than the first preset value according to described characteristic distance judges the good evil of described mammary gland tumor.
CN201510127561.2A 2015-03-23 2015-03-23 A kind of determination methods and device of the good evil of mammary gland tumor Expired - Fee Related CN104771228B (en)

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