【图像分割】基于Kmean聚类 分水岭、oust、粒子群算法优化脂肪肝图像分割附matlab代码

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智能优化算法  神经网络预测 雷达通信  无线传感器

信号处理 图像处理 路径规划 元胞自动机 无人机  电力系统

⛄ 内容介绍

Particle Swarm Optimization (PSO) is population based stochastic algorithm to form clusters with the help of fitness functions. PSO clustering algorithm is widely used in pattern recognition methods such as image segmentation where PSO defines less number of clusters compared to conventional clustering approaches. Level Sets image segmentation aided with the clustering gives fast convergence towards the desired boundaries of the object to be segmented. Here in this paper a novel approach of image segmentation using PSO clustering applied to Level sets is been presented where PSO performs better than KFCM by generating more compact clusters and larger inter cluster separation. The proposed method is successfully implemented on the images and results obtained show the effectiveness of the approach.

⛄ 部分代码

%% Fatty Liver Level Recognition Using Particle Swarm optimization (PSO) Image Segmentation and Analysis

% https://ieeexplore.ieee.org/document/9960108

% DOI: 10.1109/ICCKE57176.2022.9960108

% Please cite below:

%% -------------------------------------------------------------------------------

clear;

clc;

close all;

warning('off');

% Loading

img=imread('fat.jpg');

img=im2double(img);

imgtemp=img;

img = histeq(img);

gray=rgb2gray(img);

gray=imadjust(gray);

% Reshaping image to vector

X=gray(:);

%% Starting PSO Segmentation

k = 2; % Number of segments

%-------------------------------------------------

%% Statistics

[Accuracy, Sensitivity, Fmeasure, Precision,...

MCC, Dice, Jaccard, Specitivity] = SegPerformanceMetrics(GT, GTComp);

disp(['Accuracy is : ' num2str(Accuracy) ]);

disp(['Precision is : ' num2str(Precision) ]);

disp(['Recall or Sensitivity is : ' num2str(Sensitivity) ]);

disp(['F-Score or Fmeasure is : ' num2str(Fmeasure) ]);

disp(['Dice is : ' num2str(Dice) ]);

disp(['Jaccard is : ' num2str(Jaccard) ]);

disp(['Specitivity is : ' num2str(Specitivity) ]);

⛄ 运行结果

⛄ 参考文献

Mousavi, Seyed Muhammad Hossein, et al. “Fatty Liver Level Recognition Using Particle Swarm Optimization (PSO) Image Segmentation and Analysis.” 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE, 2022, doi:10.1109/iccke57176.2022.9960108.

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转载：https://blog.csdn.net/matlab_dingdang/article/details/128261522