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Medical image classification method based on SVM

A technology of medical imaging and classification methods, applied in the medical field, can solve the problems of difficult manual recognition and increased workload, and achieve the effects of fast calculation speed, good recognition rate and misrecognition rate

Inactive Publication Date: 2013-12-25
JIANGSU MEILUN IMAGING SYST
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  • Claims
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AI Technical Summary

Problems solved by technology

Traditional medical images use manual recognition and text classification methods. However, with the increasing number of medical images, especially the differences in race, gender, and age involved, it is becoming more and more difficult for manual recognition. and increasing workload

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  • Medical image classification method based on SVM

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Embodiment Construction

[0021] Specific embodiments of the present invention will be further described in detail below.

[0022] Such as figure 1 As shown, a system corresponding to a SVM-based medical image classification method of the present invention includes:

[0023] Raw image database, classified image database, standard model library, standard disease model library, feature recognizer and classifier.

[0024] in:

[0025] Raw image database for storing medical images and recording donor information;

[0026] Classified image database, used to classify and store medical images in the original image database according to disease types;

[0027] The standard model library is used to store medical images of standard models of classified human organs of different genders, ages, races, heights, and weights;

[0028] The standard disease model library is used to store the medical characteristics of standard disease models, including various stages of the disease.

[0029] The feature recognizer...

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Abstract

The invention discloses a medical image classification method based on SVM. The method is characterized by comprising the following steps that 1) medical images are stored in an original image data base, the medical images comprise digital photos and donor information; 2) a feature recognizer is used for recognizing the medical features of the medical images in the original image data base; 3) a classifier is used for recognizing diseases according to the medical features obtained by the feature recognizer, and the medical features are stored in a classification image data base according to the types of the diseases. According to the medical image classification method based on SVM, an Adaboost cascade classifier is used for training in advance, good recognition rate and false accept rate for different samples are achieved, computing speed is quick, recognition and classification of a large number of medical images can be carried out quickly, and the method can be used for a large-scale medical image data base system.

Description

[0001] technical field [0002] The invention belongs to the field of medicine, in particular to medical image management therein. Background technique [0003] Medical imaging is widely used in clinical diagnosis and treatment. How to use a large number of medical images to assist doctors in the diagnosis and treatment of diseases is a problem that the industry is currently studying. An excellent medical image classification method must be based on the perfect and detailed classification of disease types and donors, so as to perform efficient retrieval, information analysis and mining at any time. Traditional medical images use manual recognition and text classification methods. However, with the increasing number of medical images, especially the differences in race, gender, and age involved, it is becoming more and more difficult for manual recognition. And the workload is increasing day by day. How to solve this problem, introduce increasingly mature computer image rec...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/64
Inventor 胡边王燕妮陈波刘贵
Owner JIANGSU MEILUN IMAGING SYST
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