疾病检测基于matlab机器视觉黑色素瘤皮肤癌检测含Matlab源码 1689期

Posted 2022-02-17 紫极神光

一、数字图像处理简介

图像处理基础教程链接
1 【基础教程】基于matlab图像处理（表示方法+数据结构+基本格式+类型转换+读取+点运算+代数运算）【含Matlab源码 834期】
2 【基础教程】基于matlab图像处理（读写+显示+运算+转换+变换+增强+滤波+分析+统计）【含Matlab源码 144期】
3 【基础教程】基于matlab图像增强+复原+分割【含Matlab源码 056期】

二、部分源代码

clear all

% Parameters
out_on=0;
save_on=1;

% File tree walker
% fname='internet/atypical_naevi.jpg';
% fname='Stanford/ssm/SSM.png';
% fname='Stanford/ssm/Image_01.jpg';
% fname='Stanford/ssm/Image_06.jpg';
% fname='Stanford/normal/CS_1-26-05 B.tif';
%fname='R:\\Project\\New folder (2)\\benign1.bmp';
% fname='Stanford/normal-fp/CS_10-24-01.tif';
global im im2 img
[path,user_cance]=imgetfile();
if user_cance
    msgbox(sprintf('Error'),'Error','Error');
    return
end
im=imread(path);
im=im2double(im); %converts to double
img=im; %for backup process :)
%axes(handles.axes3);
imshow(im);


% Load image
%img=imread(fname);
%img=imresize(img,512/size(img,1));

% Convert to grayscale
imgbw=rgb2gray(img);

% Display image
if out_on
    figure(1)
    %subplot(2,2,1);
    imagesc(imgbw)
    colormap 'jet'
    title(fname,'interpreter','none')
end

%% Analyze

% Binarize using Otsu's method
img_bn=~(im2bw(imgbw,graythresh(imgbw))); % Skin lesions are darker

if out_on
    figure(2)
    imshow(img_bn)
    title('Binarized image')
end

%% Binarize using locally adaptive thresholds
% 
% % Parameters
% tl_sz=64;       % Tile Size
% lv_tsh=0;      % Local variance threshold
% gl_tsh=graythresh(imgbw);  % Uniform regions threshold (global threshold)
% 
% % Initialize variables
% nu_rgns=ones(size(imgbw,1)/tl_sz,size(imgbw,2)/tl_sz);   % Non-uniform mask
% tsh=zeros(size(imgbw,1)/tl_sz,size(imgbw,2)/tl_sz);      % Local thresholds
% imgbw=double(imgbw);
% 
% % Apply local thresholding
% for yy=1:size(imgbw,1)/tl_sz
%     for xx=1:size(imgbw,2)/tl_sz
%         % Extract a tile from the image
%         y_bnds=(yy-1)*tl_sz+1:yy*tl_sz;
%         x_bnds=(xx-1)*tl_sz+1:xx*tl_sz;
%         tl=imgbw(y_bnds,x_bnds);
%         
%         % Compute local variance
%         l_var=var(double(tl(:)));
%         
%         % Calculate threshold based on local variance
%         if l_var<lv_tsh
%             % Uniform region
%             nu_rgns(yy,xx)=0;
%             tsh(yy,xx)=gl_tsh;
%         else
%             % Non-uniform region
%             tsh(yy,xx)=graythresh(tl);
%         end
%     end
% end
% 
% % Binarize image based on local thresholding map
% tsh2=imresize(tsh,[size(imgbw,1) size(imgbw,2)],'bilinear');
% nu_rgns2=imresize(nu_rgns,[size(imgbw,1) size(imgbw,2)],'nearest');
% img_b=imgbw>tsh2;
% img_b(~nu_rgns2)=imgbw(~nu_rgns2)>gl_tsh;

%% Small region removal

img_bns=img_bn;

%Parameters
p_sz_thr=500; % Positive region threshold
n_sz_thr=1500; % Negative region threshold


% Small region removal on positive image
img_lbl=bwlabel(img_bns,4);
bins=1:max(img_lbl(:));
a=histc(img_lbl(:),bins);
blist=bins(a<p_sz_thr);
img_bns(ismember(img_lbl,blist))=0;

% img_lbl=bwlabel(img_bns,4);
% for rgn=1:size(unique(img_lbl),1)
%     idx=find(img_lbl==rgn);
%     if size(idx,1) < p_sz_thr
%         img_bns(idx)=0;
%     end
% end

% Small region removal on negative image
img_lbl=bwlabel(~img_bns,4);
bins=1:max(img_lbl(:));
a=histc(img_lbl(:),bins);
blist=bins(a<n_sz_thr);
img_bns(ismember(img_lbl,blist))=1;


% img_lbl=bwlabel(~img_bns,4);
% for rgn=1:size(unique(img_lbl),1)
%     idx=find(img_lbl==rgn);
%     if size(idx,1) < n_sz_thr
%         img_bns(idx)=1;
%     end
% end

if out_on
    figure(3)
    imshow(img_bns)
    title('Small region removal')
end

%% Identify primary region of interest

% Look for the connected component closest to center of image
img_x0=size(imgbw,2)/2;
img_y0=size(imgbw,1)/2;
img_lbl=bwlabel(img_bns,4);
stats=regionprops(img_lbl,'Centroid');
mindist=inf;
for rgn=1:numel(stats)
    dist=sqrt(((stats(rgn).Centroid(1)-img_x0)^2) + ...
        ((stats(rgn).Centroid(2)-img_y0)^2));
    if dist<mindist
        mindist=dist;
        minrgn=rgn;
    end
end

img_pr=img_bns;
img_pr(img_lbl~=minrgn)=0;

if out_on
    figure(4)
    imshow(img_pr)
    title('Primary Region of Interest')
end

%% Boundary detection

img_ed=edge(img_pr);
[edgs_x edgs_y]=ind2sub(size(img_ed),find(img_ed));

if out_on
    figure(5)
    imshow(img_ed)
    title('Edge Detection')
end

三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 蔡利梅.MATLAB图像处理——理论、算法与实例分析[M].清华大学出版社，2020.
[2]杨丹,赵海滨,龙哲.MATLAB图像处理实例详解[M].清华大学出版社，2013.
[3]周品.MATLAB图像处理与图形用户界面设计[M].清华大学出版社，2013.
[4]刘成龙.精通MATLAB图像处理[M].清华大学出版社，2015.