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Method for segmenting brain tumor on the basis of deep belief network

A deep trust network and brain tumor technology, applied in the field of brain tumor segmentation based on a deep trust network, can solve problems such as single, neglected segmentation, etc.

Inactive Publication Date: 2017-05-31
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

At the same time, these methods have certain limitations and most of them are used for single tumor segmentation, ignoring the segmentation of necrosis and edema regions in complete brain tumors.

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  • Method for segmenting brain tumor on the basis of deep belief network

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Embodiment Construction

[0012] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be noted that in the following description, the introduction of known functions and designs related to the present invention may be downplayed and omitted.

[0013] In this embodiment, the brain tumor segmentation method of the present invention mainly includes the following links:

[0014] 1. Image preprocessing, 2. Image block extraction and data set generation, 3. Multi-classification of complete brain tumors, 4. Fuzzy C-means clustering and segmentation

[0015] Image preprocessing uses filtering, histogram equalization and brightness transformation, image extraction image blocks to generate input data sets, innovative use of deep trust network to classify complete brain tumors according to edema, necrosis and tumor areas, and map the classification model to genera...

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Abstract

The invention discloses a method based on a deep belief network to segment a brain tumor so as to be favorable for the auxiliary diagnosis of a patient brain tumor disease. The method comprises the following steps that: firstly, utilizing an adaptive filter and histogram equalization and brightness transformation to process an original image to reduce the noise of the original image and enhance an image contrast ratio; then, extracting image blocks from the processed image to generate a data set; utilizing the deep belief network to classify edema, necrosis and tumor regions in the integral brain tumor by the deep belief network to realize preliminary segmentation; and finally, utilizing fuzzy C-means clustering segmentation to carry out further accurate segmentation on the image to obtain an integral brain tumor segmentation result.

Description

technical field [0001] The invention belongs to the category of computerized medical image classification recognition and segmentation, more specifically, relates to a brain tumor segmentation method based on a deep trust network, which is used to assist medical treatment to realize automatic segmentation of brain tumors. Background technique [0002] In today's society, brain tumors are increasingly threatening people's health. Statistics show that the number of patients with brain tumors is increasing day by day. Due to the complex structure and important function of the brain, clinically, brain tumors and their tumor-like changes come in various forms, which limits the early diagnosis and treatment of brain tumors. With the advancement of computer-aided medical diagnosis technology, it has been successfully used in the auxiliary diagnosis of breast and lung lesions. If the computer can be used to achieve accurate segmentation of medical images, it will be beneficial for...

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Application Information

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IPC IPC(8): G06T7/00G06T7/10
CPCG06T7/0012G06T2207/30096
Inventor 秦臻秦志光李雪瑞
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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