【图像分割】基于改进的主动轮廓模型实现图像分割附matlab代码

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本文介绍了一种新的基于预测理论的图像分割分布测度，通过标准差量化密度函数间的差异。通过几何主动轮廓方法，我们将该测度融入算法，并与流行的Fisher信息度量进行理论对比。实证分析显示，不同类型的测度对分割结果有显著影响，特别在Kaposi's Sarcoma病理图像上进行了对比展示。算法在医疗图像挑战中表现出有效性，证实了新测度在图像分割领域的实用价值。

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⛄ 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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