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Mammary gland image feature fusion method based on restricted Boltzmann machine

A Boltzmann machine and image feature technology, applied in computer parts, instruments, characters and pattern recognition, etc., can solve the problems of obvious noise, complex components, and unsatisfactory effect of medical image feature-level fusion.

Active Publication Date: 2016-12-07
FUZHOU UNIV
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Problems solved by technology

Medical images contain characteristics such as heterogeneity, complex components, significant noise, and physiological correlations, which make the effect of feature-level fusion of medical images so far unsatisfactory.

Method used

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  • Mammary gland image feature fusion method based on restricted Boltzmann machine
  • Mammary gland image feature fusion method based on restricted Boltzmann machine
  • Mammary gland image feature fusion method based on restricted Boltzmann machine

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Embodiment Construction

[0048] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0049] A kind of mammary gland image feature fusion method based on restricted Boltzmann machine of the present invention, such as figure 1 As shown, it specifically includes the following steps:

[0050] Step S1: For mammography images, the manual shallow features extracted from different perspectives can be obtained after deep learning to obtain high-level semantic features from different perspectives;

[0051] Step S2: For breast B-ultrasound images, the manual shallow features extracted from different perspectives can be obtained after deep learning to obtain high-level semantic features from different perspectives;

[0052] Step S3: After cascading the high-level semantic features of different perspectives of the mammography images, the high-level semantic features after multi-view fusion of the mammography images are obtained;

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Abstract

The invention relates to a mammary gland image feature fusion method based on a restricted Boltzmann machine. The method first obtains the high-level semantic feature of a mammary gland molybdenum target and a mammary gland B-scan ultrasonography by stacked self-encoder deep learning, and then uses a restricted Boltzmann Machine model to extract the shared feature representation of different modes of image of the mammary gland B-scan ultrasonography and the mammary gland molybdenum target. The mammary gland image feature fusion method based on the restricted Boltzmann machine obtains the united distribution between different modal statistical attributes of the mammary gland B-scan ultrasonography and mammary gland molybdenum target by a unsupervised training mode, and can fully utilize a large amount of unlabeled data, compensates a defect of excessive reliance on sample class labels of a lot of conventional feature fusion method, and has strong practicality.

Description

technical field [0001] The invention relates to the technical field of feature engineering, in particular to a breast image feature fusion method based on a restricted Boltzmann machine. Background technique [0002] Breast cancer is one of the most common malignant tumors occurring in women. In recent years, my country's investigations and studies have shown that the incidence of breast cancer is increasing year by year. Therefore, it is more and more meaningful to improve the accuracy of early diagnosis of breast cancer. [0003] At present, the main method used in the diagnosis of breast cancer is through imaging examinations such as mammography and B-ultrasound images, and the diagnoser analyzes the condition through imaging features such as calcification or mass. However, because the density of soft tissue such as glands, blood vessels, and fat in breast tissue is very close to the density of the lesion area, coupled with factors such as visual fatigue of the diagnost...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06V2201/03G06F18/253
Inventor 王秀余春艳滕保强陈壮威
Owner FUZHOU UNIV
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