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Medical image multi-threshold segmentation method based on improved dogvessel colony algorithm

A medical image and salp group technology, which is applied in the field of multi-threshold segmentation of medical images based on the improved salp group algorithm, can solve the problems of falling into local optimum, lower threshold image segmentation accuracy, and premature convergence, etc. sticky effect

Pending Publication Date: 2021-11-05
WENZHOU UNIVERSITY
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Problems solved by technology

However, during the search process of SSA, there is still a phenomenon of falling into local optimum and premature convergence, which will eventually lead to a decrease in the accuracy of threshold image segmentation.

Method used

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  • Medical image multi-threshold segmentation method based on improved dogvessel colony algorithm
  • Medical image multi-threshold segmentation method based on improved dogvessel colony algorithm
  • Medical image multi-threshold segmentation method based on improved dogvessel colony algorithm

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Embodiment

[0054] Embodiment: a kind of medical image multi-threshold segmentation method based on improved salp group algorithm comprises the following steps:

[0055] Step S1, denote the medical image to be segmented as I, its size is denoted as m×n, the pixel point of row i and column j in medical image I is denoted as (i, j), and the pixel point of medical image I (i , the gray value of j) is recorded as a i，j , i=1, 2,..., m, j=1, 2,..., n, set the number L=20 of the thresholds for segmenting the medical image; the medical image I is a grayscale image, such as figure 1 shown;

[0056] Step S2, first perform non-local mean value filtering on the medical image I to obtain a non-local mean value image with a size of m×n, and record the pixel point in row i and column j of the non-local mean value image as (i n , j n ), the pixels in the non-local mean image (i n , j n ) gray value is recorded as i n =1,2,...,m,j n =1,2,...,n,

[0057] The pixel point in row i and column j in ...

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Abstract

The invention discloses a medical image multi-threshold segmentation method based on an improved doliolaria glans group algorithm. The method comprises the following steps: constructing a two-dimensional histogram by adopting a gray image and a non-local mean image of a medical image, determining threshold selection of a Kapur entropy threshold method by using the doliolaria glans group algorithm, and in the threshold selection process, improving and mutating a doliolaria group algorithm by using an individual linkage mutation strategy to avoid falling into local optimum, so that the segmentation effect of the medical image is optimal. The method has the advantages of good robustness and high accuracy.

Description

technical field [0001] The invention relates to a medical image multi-threshold segmentation method, in particular to a medical image multi-threshold segmentation method based on an improved salp group algorithm. Background technique [0002] Image segmentation is the key technology of image preprocessing, the key step from image processing to image analysis, and also a difficult problem in computer vision, image analysis and image understanding. As an important application field of medical image processing, medical image segmentation can be divided into assisting doctors in formulating treatment plans, locating diseased tissue areas, and anatomical tissue research. However, with the continuous development of medical image imaging equipment and imaging technology, doctors often need to invest a lot of time and energy to interpret medical images one by one. Sex will affect the doctor's ability to correctly diagnose and treat a patient's condition. [0003] The threshold ima...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T7/136G06N3/00
CPCG06T7/0012G06T7/136G06T7/11G06N3/006G06T2207/10004G06T2207/20021G06T2207/30004G06T7/143G06T2207/30024
Inventor 汪鹏君赵松伟陈慧灵许素玲何文明李刚
Owner WENZHOU UNIVERSITY
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