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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

⛄ 内容介绍

In this paper, we present a new distribution metric for image segmentation that arises as a result in prediction theory. Forming a natural geodesic, our metric quantifies "distance" for two density functionals as the standard deviation of the difference between logarithms of those distributions. Using level set methods, we incorporate an energy model based on the metric into the Geometric Active Contour framework. Moreover, we briefly provide a theoretical comparison between the popular Fisher Information metric, from which the Bhattacharyya distance originates, with the newly proposed similarity metric. In doing so, we demonstrate that segmentation results are directly impacted by the type of metric used. Specifically, we qualitatively compare the Bhattacharyya distance and our algorithm on the Kaposi Sarcoma, a pathology that infects the skin. We also demonstrate the algorithm on several challenging medical images, which further ensure the viability of the metric in the context of image segmentation.

⛄ 部分代码

clear all

close all

clc

img=double(dicomread('IM0.dcm'));%读取图像

iter = 1000;

alpha = .05;

dt = .4;

flag_approx = 0;

[M,N]=size(img);

mask=zeros(M,N);

mask(200:400,100:400)=1;

% %read img path

% img = double(imread(img_path));

if(size(img,3)~=1);

    disp(['WARNING:  Code only works for gray scale, please modify '...,

        'code. Will continue will process input image as gray scale.']);

    img = (img(:,:,1)+img(:,:,2)+img(:,:,3))/3;

end

%if no pre_mask given, display image so user can select initialization

if(~exist('mask','var'));

    imshow(img,'InitialMagnification',200);

    mask = double(get_rect_mask(img));

else

    mask = mask;

end

%compute sdf function

phi = bwdist(mask) - bwdist(1-mask)+im2double(mask);

%run active contour

[result,pin,pout] = Tryphon_NB(img, phi, iter, dt, alpha,flag_approx);

⛄ 运行结果

⛄ 参考文献

[1] Sandhu R ,  Georgiou T ,  Tannenbaum A . A New Distribution Metric for Image Segmentation[C]// Imaging Processing pt.1. School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250;Department of Electrical & Computer Engineering, University of Minnesota, Minneapolis, MN 55455, 2008.

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