Lung malignant tumor MRI identification method utilizing maximum class separation distance method of genetic algorithm

A technology of maximum inter-class and genetic algorithm, applied in the field of image processing, can solve the problem of insufficient accuracy of segmented images, and achieve high accuracy and high recognition accuracy

Inactive Publication Date: 2015-12-23
ZHEJIANG GONGSHANG UNIVERSITY
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Problems solved by technology

However, the accuracy of segmented images obtained by this method is still not high enough.

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  • Lung malignant tumor MRI identification method utilizing maximum class separation distance method of genetic algorithm
  • Lung malignant tumor MRI identification method utilizing maximum class separation distance method of genetic algorithm
  • Lung malignant tumor MRI identification method utilizing maximum class separation distance method of genetic algorithm

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Embodiment Construction

[0028] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but the present invention is not limited to the following specific embodiments.

[0029] A method for MRI identification of lung malignancies using the method of maximum inter-class distance using genetic algorithms, that is, an MRI image segmentation method using genetic algorithms for maximum inter-class distance in the process of identifying lung malignancies, which is essentially a The segmentation optimization method of lung MRI image is characterized in that it comprises the following steps:

[0030] (1), establish the standard signal-to-noise ratio data collection of known lung MRI images;

[0031] A, segment a plurality of lung MRI images by traditional method; Described traditional method is mark watershed method, also can be other conventional image segmentation methods;

[0032] B. Judging by the doctor's naked eyes whether the image ...

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Abstract

The invention relates to the technical field of image processing, and specifically relates to a lung malignant tumor MRI identification method utilizing a maximum class separation distance method of a genetic algorithm. For the lung malignant tumor MRI identification method, a non-linear genetic array optimization model is added, and optimization of segmented images can be performed through the non-linear genetic array optimization model. Therefore, the segmented images obtained through the method are high in accuracy so that the identification of lung tumour is higher in accuracy.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an MRI identification method for lung malignant tumors using the maximum inter-class distance method introduced with a genetic algorithm. Background technique [0002] Medical image segmentation is an indispensable means of extracting quantitative information of special tissues in images, and it is also a prerequisite for 3D reconstruction and visualization of images. Segmented images are widely used in various occasions, such as location and diagnosis of diseased tissues, learning of anatomical structures, computer-guided surgery and 3D visualization, etc. [0003] MRI (magnetic resonance imaging) images are one of the important components of medical images, but because MRI images have a certain degree of noise, we need to preprocess the original MRI images in order to obtain better image quality and improve processing accuracy , to obtain the ideal segmentation effect...

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Application Information

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IPC IPC(8): G06T7/00G06N3/12
CPCG06N3/126G06T2207/10088G06T2207/30061
Inventor 汤旭翔傅均陈赛陈柳柳
Owner ZHEJIANG GONGSHANG UNIVERSITY
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