Brain tumor segmentation method and device in combination with convolutional neural network and fuzzy inference

A convolutional neural network and fuzzy reasoning technology, applied in the field of medical devices, can solve problems such as low efficiency and instability

Inactive Publication Date: 2017-09-22
TIANJIN UNIV
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Problems solved by technology

[0018] In order to overcome the deficiencies of the prior art, the present invention aims to apply computer image processing technology to the segmentation of brain tumor MRI images, avoid the defects of low efficiency and instab

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  • Brain tumor segmentation method and device in combination with convolutional neural network and fuzzy inference
  • Brain tumor segmentation method and device in combination with convolutional neural network and fuzzy inference
  • Brain tumor segmentation method and device in combination with convolutional neural network and fuzzy inference

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Embodiment Construction

[0051] The main purpose of the present invention is to apply computer image processing technology in the segmentation of brain tumor MRI images, avoid the low efficiency and unstable defects of manual segmentation, and use computer algorithms to provide fast and reliable brain tumor segmentation results. Provide accurate basis for tumor diagnosis, treatment and surgical guidance.

[0052] The present invention performs probability prediction on two types of unimodal images through a convolutional neural network model, and the obtained probability prediction results are processed by nonlinear mapping and then used as input to a fuzzy reasoning system to determine whether a pixel belongs to a tumor area. In the given image library test, the invention can effectively complete the segmentation and extraction of brain tumor MRI images, and has good theoretical and application value.

[0053] In order to achieve the above object, the present invention adopts the following technical ...

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Abstract

The invention relates to medical instrument, applies a computer image processing technology to segmentation of a brain tumor magnetic resonance image, avoids the defects of low efficiency and instability existing in manual segmentation, provides a rapid and reliable brain tumor segmentation result by use of a computer algorithm and provides an accurate basis for diagnosis, treatment and surgical guidance of a brain tumor. According to the technical scheme, a brain tumor segmentation method in combination with a convolutional neural network and fuzzy inference comprises the following steps of (1) selecting an image; (2) constructing a CNN (Convolutional Neural Network) model; (3) carrying out nonlinear mapping; and (4) establishing a fuzzy inference system. The brain tumor segmentation method in combination with the convolutional neural network and fuzzy inference is mainly applied to processing of medical images.

Description

technical field [0001] The invention relates to a medical device, which combines medical images and computer algorithms to complete the segmentation of nuclear magnetic resonance images of brain tumors. Specifically, it involves a brain tumor segmentation method and device that combines convolutional neural networks and fuzzy reasoning. Background technique [0002] Brain tumors are divided into benign tumors and malignant tumors. Benign tumors can generally recover after surgical treatment. Malignant tumors are also called brain cancer because they are stubborn and difficult to cure. According to the survey, 23,000 new brain tumors were diagnosed in the United States in 2015 alone. Brain tumors have seriously endangered human life and health, and how to better diagnose and treat them is very important. At present, the main means of checking the impact of brain tumors are magnetic resonance imaging (magnetic resonance imaging, MRI) and computed tomography (computed tomogra...

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Application Information

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IPC IPC(8): G06T7/11G06T7/143
CPCG06T7/11G06T7/143G06T2207/10088G06T2207/20081G06T2207/20084G06T2207/30016G06T2207/30096
Inventor 师冬丽李锵关欣
Owner TIANJIN UNIV
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