CN109635846A - A kind of multiclass medical image judgment method and system - Google Patents

A kind of multiclass medical image judgment method and system Download PDF

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CN109635846A
CN109635846A CN201811368056.7A CN201811368056A CN109635846A CN 109635846 A CN109635846 A CN 109635846A CN 201811368056 A CN201811368056 A CN 201811368056A CN 109635846 A CN109635846 A CN 109635846A
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曾国坤
王蓝天
曾品榛
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Shenzhen Graduate School Harbin Institute of Technology
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Abstract

The invention discloses a kind of multiclass medical image judgment method and system, method includes processing medical image to obtain characteristics of image, analyzes different types of medical image, and the classifier based on random deep woods is to obtain optimal feature subset；Handling optimal feature subset by support vector machines with output category result and assigns medical image corresponding type label；Corresponding disease image Processing Algorithm is selected to handle the medical image again to obtain genius morbi, based on sorter network processing genius morbi to export diagnostic result based on type label.System is for executing corresponding method.The present invention, to obtain characteristics of image, obtains optimal feature subset based on classifier by processing medical image；Pass through support vector machines output category result, select disease image Processing Algorithm processing medical image to obtain genius morbi according to classification results, based on sorter network processing genius morbi to export diagnostic result, it can be improved Medical Image Processing speed, reduce the processing load of doctor.

Description

A kind of multiclass medical image judgment method and system
Technical field
The present invention relates to technical field of image processing, especially a kind of multiclass medical image judgment method and system.
Background technique
In clinic, the most of of medical data are medical image, and with the update of image acquisition technology and equipment at This reduction, from now in the medical field, the importance of medical image can deepen, and the quantity generated during diagnosis can also increase Add.Meanwhile the quantity of skilled medical practitioner will not increase significantly, in this case, identification medical image will for doctor The too many time is occupied, working efficiency is caused to reduce, is unfavorable for medical diagnosis, increases the probability of happening of malpractice.
Meanwhile various medical images is many kinds of, the image of different types has each different feature, it is desirable to ripe Practice that grasp the identification of image of all or most be very difficult thing, if either increase image recognition personnel or The training burden that person increases the image recognition of doctor is all non-efficiency and valuableness, it is therefore desirable to which one can reduce conscientiously doctor and sentence The method of disconnected medical image.
Summary of the invention
The present invention is directed to solve at least some of the technical problems in related technologies.For this purpose, of the invention One purpose is to provide a kind of multiclass medical image judgment method and system.
The technical scheme adopted by the invention is that: a kind of multiclass medical image judgment method, comprising steps of processing medicine figure As to obtain characteristics of image, the classifier based on random deep woods screens described image feature to obtain optimal feature subset；Pass through The support vector machines processing optimal feature subset is with output category result and assigns the medical image corresponding type label； Corresponding disease image Processing Algorithm is selected to handle the medical image again to obtain genius morbi based on the type label, Based on the sorter network processing genius morbi to export diagnostic result.
Further, the sorter network is based on nearest neighbor algorithm and/or handles institute in random deep woods and/or BP neural network Genius morbi is stated to export diagnostic result.
Further, the step of processing medical image is to obtain characteristics of image includes: based on airspace or frequency domain filtering The denoising for carrying out medical image handles medical image to realize the segmentation of area-of-interest, based on preset based on template matching Feature identification model handles area-of-interest to obtain characteristics of image.
Further, the step of classifier of the random deep woods screens described image feature includes: according to characteristics of image Subcharacter setting corresponding number Random Forest model, calculate the different degree of each subcharacter, reject different degree and be less than threshold The subcharacter of value is to obtain optimal feature subset.
Further, it is suitable for plurality of medical diagnostic imaging, such as: galactophore image, lung, ultrasonic wave, blood cell and sperm etc., Comprising steps of carrying out the denoising of medical image based on airspace or frequency domain filtering, medicine figure is handled based on sech template matching algorithm As to determine area-of-interest, the segmentation of area-of-interest is realized based on watershed algorithm and RSF model.
It is of the present invention another solution is that a kind of multiclass medical image judges system, comprising: primary module, For handling medical image to obtain characteristics of image, the classifier screening described image feature based on random deep woods is optimal to obtain Character subset；Secondary module, for handling the optimal feature subset by support vector machines with output category result and assigning The corresponding type label of the medical image；Final module, for being selected at corresponding disease image based on the type label Adjustment method handles the medical image again to obtain genius morbi, is examined based on the sorter network processing genius morbi with exporting Disconnected result.
Further, the sorter network is based on nearest neighbor algorithm and/or handles institute in random deep woods and/or BP neural network Genius morbi is stated to export diagnostic result.
Further, the step of processing medical image is to obtain characteristics of image includes: based on airspace or frequency domain filtering The denoising for carrying out medical image handles medical image to realize the segmentation of area-of-interest, based on preset based on template matching Feature identification model handles area-of-interest to obtain characteristics of image.
Further, the step of classifier of the random deep woods screens described image feature includes: according to characteristics of image Subcharacter setting corresponding number Random Forest model, calculate the different degree of each subcharacter, reject different degree and be less than threshold The subcharacter of value is to obtain optimal feature subset.
Further, the primary module is also used to be carried out the denoising of medical image based on airspace or frequency domain filtering, is based on Sech template matching algorithm handles medical image to determine area-of-interest, based on watershed algorithm and RSF model to realize sense The segmentation in interest region.
The beneficial effects of the present invention are: the present invention handles medical image to obtain characteristics of image, based on dividing for random deep woods Class device screens described image feature to obtain optimal feature subset；The optimal feature subset is handled by support vector machines with defeated Classification results out select corresponding disease image Processing Algorithm to handle the medical image again to obtain disease according to classification results Sick feature can be improved Medical Image Processing speed, drop based on the sorter network processing genius morbi to export diagnostic result The processing load of low doctor.
Detailed description of the invention
Fig. 1 is the schematic diagram of medical image judgment method of the invention；
Fig. 2 is a kind of schematic diagram of Medical Image Processing frame of the invention.
Specific embodiment
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase Mutually combination.
Embodiment 1
The present embodiment is for the shortcomings that illustrating the prior art and resolving ideas of the invention
It is quite reasonable selection that intracorporal internal organ are detected by way of microwave imaging, because not will cause the damage of internal organ Wound, will not cause the trace of body surface；Thus derive the method for the various health status by medical image acquisition human body, and Reality prove these methods be it is effective, with and come be image data amount caused by the broader applications of medical image increasing Add.
In the case where artificial intelligence is not able to achieve high resolution yet, in fact, the confirmation of the last state of an illness there is still a need for by Mankind doctor is responsible for, this just brings very big homework burden to doctor, and the fatigue on body can bring the decline of recognition capability, It is very big unfavorable that this can bring to the diagnosis of the state of an illness, leads to malpractice.
Therefore, the present invention provides a kind of multiclass medical image judgment method as shown in Figure 1, comprising steps of S1, processing doctor Image is learned to obtain characteristics of image, based on the classifier screening described image feature of random deep woods to obtain optimal feature subset； S2, the optimal feature subset is handled by support vector machines with output category result and assigns the medical image corresponding class Type label；S3, corresponding disease image Processing Algorithm is selected to handle the medical image again to obtain based on the type label Genius morbi is taken, based on the sorter network processing genius morbi to export diagnostic result.
Its principle is that there are many existing algorithm for being related to medical image, this programme be extract these schemes it is common The step of, the identification of primary is realized according to these steps, after completing primary identification, is being carried out once according to special algorithm Processing is to obtain better treatment effect.
Wherein, handle medical image (referred to as image) comprising steps of
Denoising, for Image filter arithmetic, is generally divided into two types, one is airspace filters, another is then In frequency domain filtering, the present embodiment, using image filtering is carried out on airspace, (frequency domain filtering algorithm is on the medical image Good effect can be obtained), under normal circumstances, being filtered to medical image is for smoothed image, and therefore, we make mostly The pretreatment of medical image is carried out with median filtering, mean filter, wherein median filtering is the surrounding neighbors collection with pixel The intermediate value of gray value replaces the gray value at preimage vegetarian refreshments in conjunction, and the removal for salt-pepper noise and patch noise, effect is very Obviously, and mean filter be then using the gray value in pixel surrounding neighbors set mean value replace preimage vegetarian refreshments gray scale Value.
Template matching, template matching algorithm are a kind of common methods of basic target positioning in pattern-recognition, mainly It is by definition template image, the similarity of the sub-image area of calculation template image and target image finds target position； It, generally can be using circular shuttering, spherical template, Gaussian template and hyperbolic just for the suspicious lesions zone location of medical image Template is cut, the specific function that handles includes Gaussian function and hyperbolic tangent function (sech function), wherein Gaussian function is relatively more suitable Detection microcalcifications are closed, and hyperbolic tangent function is suitble to detect lump.Therefore, the present embodiment uses sech template matching to calculate Method is difficult with a kind of template matching of size to all lesion regions simultaneously because the shape of lesion region, different sizes. Therefore, the template matching based on single size can not detect all lesion regions well.Therefore, more rulers can be taken Degree strategy, constructs the template with sizes size, matches to target image, arrive respectively to multiple dimensioned template detection As a result it is merged, obtains final testing result (determining lesion region).
Feature identification carries out the extraction of feature after the determination for completing above-mentioned lesion region, these features include doctor The number of 4 connected regions after learning the dimension of image, gray average, variance, binary conversion treatment and number, the face of 8 connected regions Colour moment, gray level co-occurrence matrixes, histograms of oriented gradients and local binary patterns etc. (i.e. subcharacter)；These features are all in image Commonly used index or parameter in processing, acquisition methods are more, and the present embodiment is without further explanation.
After carrying out feature extraction to medical image, obtained feature vector dimension is very big, it means that calculation amount It is sizable.Therefore, we will carry out feature selecting, screen by random forest to each feature, and rejecting is not So important feature obtains optimal feature subset.In addition, the algorithm for Medical Images Classification has very much, such as commonly Three kinds of classifiers, k nearest neighbor (k-NearestNeighbor, KNN), support vector machines and neural network classifier, for extraction To feature vector classified (i.e. judgement belongs to the image of which kind of disease), the present embodiment chooses support vector machines as classification Device is used for output category result.
After completing above-mentioned judgement medical image, then corresponding Processing Algorithm can be selected according to specific disease type, The present embodiment is by taking the medical image of mammary gland as an example, after completing above-mentioned deterministic process:
It takes the mode of median filtering to realize denoising first, passes through sech template matching algorithm calculation template image and target Similarity graph picture between image to similarity graph as binary conversion treatment, then passes through a series of preset rules removal false positive Region, remaining region are the suspicious mass region (i.e. area-of-interest) detected.
Then being handled by preset feature identification model can be with lump region, wherein feature identification model is in the nature The recognizer of above-mentioned various features, the technical field of the acquisitions of the parameters such as dimension, gray average, variance in image procossing It is open more sufficient basic technology, the present embodiment is without additional explanation.
Finally by trained classifier, (i.e. sorter network is carried out by disease datas such as MIAS data sets Training is to obtain the ability of classification) carry out feature classification (this subseries the result is that diagnostic result, technological essence are bases The degree of similitude exports a corresponding value).
Embodiment 2
For the present embodiment for explaining preset rules on the basis of embodiment 1, the purposes of the preset rules is at image A part of content/data of exclusion of selectivity, specific rule include: during reason
1) region area:, can by the lump diameter in analysis MIAS data set (i.e. William Kang Xing breast cancer data set) To judge the possible area of lump in a certain range, pectoral region is likely to more than this range；
2) shape: being more than centainly using the length-width ratio of rectangle as form factor according to the minimum outsourcing rectangle of candidate region Size then can remove；
3) gray average: the pixel value in lump region is generally relatively high, then can determine that the region not lower than certain threshold value It is lump；
4) gray variance: the variance in lump region generally all less (is not more than certain threshold value)；
5) morphological feature: eccentricity and circularity.
By the formulation of above-mentioned rule, false positive region can be removed and filter out suspicious region.
It is above-mentioned filter out suspicious region on the basis of, in addition the present embodiment proposes a kind of watershed of binding marker control The method that algorithm and RSF model combine is to realize the segmentation for lump:
Area-of-interest is split using the watershed algorithm of marking of control, obtains the approximate location in lump region；
Extract the boundary in lump region, the coordinate of record delimitation point；
The closed curve that the boundary point coordinate recorded is formed is used as the initial profile line of RSF model RSF model, which is finely divided, to be cut, and accurate lump profile is obtained.
Embodiment 3
The present embodiment provides a kind of Medical Image Processing frames as shown in Figure 2, comprising:
As the medical image databases in medical image source, from the various features of medicine image zooming-out (including color moment, Gray level co-occurrence matrixes, histograms of oriented gradients and local binary patterns etc.), various features are passed through into random forest Fusion Features, Classification processing based on SVM classifier exports the classification (i.e. class label) of image, according to class label, from disease image Reason algorithm data-base chooses suitable Processing Algorithm to handle medical image and obtain various features (including color moment, gray scale are total Raw matrix, histograms of oriented gradients and local binary patterns etc., at this point, marking it for genius morbi), then according to KNN, (K is close It is adjacent), SVM (support vector machines), BPNN (neural network) classifier (any combination) classified (i.e. output judging result).
It is to be illustrated to preferable implementation of the invention, but the invention is not limited to the implementation above Example, those skilled in the art can also make various equivalent variations on the premise of without prejudice to spirit of the invention or replace It changes, these equivalent deformations or replacement are all included in the scope defined by the claims of the present application.

Claims (10)

1. a kind of multiclass medical image judgment method, which is characterized in that comprising steps of
Medical image is handled to obtain characteristics of image, the classifier screening described image feature based on random deep woods is optimal to obtain Character subset；
The optimal feature subset is handled with output category result by support vector machines and to assign the medical image corresponding Type label；
Corresponding disease image Processing Algorithm is selected to handle the medical image again to obtain disease based on the type label Feature, based on the sorter network processing genius morbi to export diagnostic result.
2. a kind of multiclass medical image judgment method according to claim 1, which is characterized in that the sorter network is based on Nearest neighbor algorithm and/or the genius morbi is handled to export diagnostic result in random deep woods and/or BP neural network.
3. a kind of multiclass medical image judgment method according to claim 1 or 2, which is characterized in that the processing medicine The step of image is to obtain characteristics of image include:
The denoising that medical image is carried out based on airspace or frequency domain filtering, it is interested to realize based on template matching processing medical image The segmentation in region, based on preset feature identification model processing area-of-interest to obtain characteristics of image.
4. a kind of multiclass medical image judgment method according to claim 3, which is characterized in that point of the random deep woods Class device screen described image feature the step of include:
The Random Forest model of corresponding number is set according to the subcharacter of characteristics of image, calculates the different degree of each subcharacter, It rejects different degree and is less than the subcharacter of threshold value to obtain optimal feature subset.
5. a kind of multiclass medical image judgment method according to claim 3, comprising steps of
The denoising that medical image is carried out based on airspace or frequency domain filtering, based on sech template matching algorithm processing medical image with true Determine area-of-interest, the segmentation of area-of-interest is realized based on watershed algorithm and RSF model.
6. a kind of multiclass medical image judges system characterized by comprising
Primary module, for handling medical image to obtain characteristics of image, the classifier based on random deep woods screens described image Feature is to obtain optimal feature subset；
Secondary module, for handling the optimal feature subset by support vector machines with output category result and assigning the doctor Learn the corresponding type label of image；
Final module, for selecting corresponding disease image Processing Algorithm to handle the medicine figure again based on the type label As handling the genius morbi based on sorter network to export diagnostic result to obtain genius morbi.
7. a kind of multiclass medical image according to claim 6 judges system, which is characterized in that the sorter network is based on Nearest neighbor algorithm and/or the genius morbi is handled to export diagnostic result in random deep woods and/or BP neural network.
8. a kind of multiclass medical image according to claim 6 or 7 judges system, which is characterized in that the processing medicine The step of image is to obtain characteristics of image include:
The denoising that medical image is carried out based on airspace or frequency domain filtering, it is interested to realize based on template matching processing medical image The segmentation in region, based on preset feature identification model processing area-of-interest to obtain characteristics of image.
9. a kind of multiclass medical image according to claim 8 judges system, which is characterized in that point of the random deep woods Class device screen described image feature the step of include:
The Random Forest model of corresponding number is set according to the subcharacter of characteristics of image, calculates the different degree of each subcharacter, It rejects different degree and is less than the subcharacter of threshold value to obtain optimal feature subset.
10. a kind of multiclass medical image according to claim 8 judges system, it is suitable for claim 5 the method, It is characterized in that, the primary module, is also used to carry out the denoising of medical image based on airspace or frequency domain filtering, is based on sech template Matching algorithm handles medical image to determine area-of-interest, based on watershed algorithm and RSF model to realize area-of-interest Segmentation.
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