Mammography Image Feature extraction

Question

Mohamed Ahmed 2017-7-26

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/350414-mammography-image-feature-extraction

回答： AHT.fatima 2022-5-12

PIC1.png

I'm trying to extract texture features of a tumor (ROI) for mammography classification.

The features I'm trying GLCM features. I tried two codes, but the results of classification is not good. The classifiers are working with other mammography data, so the problem is the extracted features.

So, i need to know what is wrong through these steps?

The first code i tried is the code in this link:

https://www.mathworks.com/matlabcentral/fileexchange/22187-glcm-texture-features?focused=5107518&tab=function

and called the function by the following:

    GLCM = graycomatrix(ROI,'Offset',[0 1;-1 1;-1 0;-1 -1]);
    [stats,feat] = GLCM_Features1(GLCM,0);
    GLCM_Diff_Cal1(l,:)=feat;

Another code I tried is:

GLCM = graycomatrix(ROI,'Offset',[0 1;-1 1;-1 0;-1 -1]);
stats=graycoprops(GLCM2,{'Contrast','Homogeneity','Correlation','Energy'});
contrast=(stats.Contrast);
en=(stats.Energy);
co=(stats.Correlation);
hom=(stats.Homogeneity);
m=mean(mean(ROI));
s=std2((ROI));
f1=[m s hom co en contrast];
J2=SegmentedROI;
I=J2;
J2 = uint8(255 * mat2gray(J2));
J3=edge(J2,'log');
bw=bwareaopen(J2,150);                          
bwp=edge(bw,'sobel');
geometric.area=sum(sum(bw));
geometric.peri=sum(sum(bwp));
geometric.compact=geometric.peri^2/geometric.area;
count=1;
roih=0;
for i=1:size(bw,1);
      for j=1:size(bw,2);
          if bw(i,j)
              roih(count,1)=double(SegI(i,j)); 
              count=count+1;
          end
      end
  end
nh=roih/geometric.area;
texture.mean=mean(roih);
texture.globalmean=texture.mean/mean(mean(double(I)));
texture.std=std(roih);
texture.smoothness=1/(1+texture.std^2);
texture.uniformity=sum(nh.^2);
texture.entropy=sum(nh.*log10(nh));
texture.skewness=skewness(roih);
texture.correlation=sum(nh'*roih)-texture.mean/texture.std;
x1=[];
y1=[];
z1=[];
[gradI3, Gdir] = imgradient(I,'sobel');
gradJ3=medfilt2(gradI3);
gradJ2=uint8(255 * mat2gray(gradJ3));
gradJ3=edge(gradJ2,'log');
gradbw=bwareaopen(gradJ2,250);
gradbwp=edge(gradbw,'sobel');
count=1;
for i=1:size(gradbw,1);
      for j=1:size(gradbw,2);
          if gradbw(i,j)
              gradroih(count,1)=double(gradI3(i,j)); 
              count=count+1;
          end
      end
  end
gradroih(gradroih==0)=[];
gradnh=gradroih/geometric.area;
gradient.mean=mean(gradroih);
gradient.globalmean=texture.mean/mean(mean(double(gradI3)));
gradient.std=std(roih);
gradient.smoothness=1/(1+gradient.std^2);
gradient.uniformity=sum(gradnh.^2);
gradient.entropy=sum(gradnh.*log10(gradnh));
gradient.skewness=skewness(gradroih);
gradient.correlation=sum(gradnh'*gradroih)-gradient.mean/gradient.std;
geometric
texture
gradient
xy=[geometric.area,geometric.peri,geometric.compact];
x1=[x1;xy];
yy=[texture.mean,texture.globalmean,texture.std,texture.smoothness,texture.uniformity,...
    texture.entropy,texture.skewness,texture.correlation];
y1=[y1;yy];
zy=[gradient.mean,gradient.globalmean,gradient.std,gradient.smoothness,gradient.uniformity,...
    gradient.entropy,gradient.skewness,gradient.correlation];
z1=[z1;zy];
f2=[x1 y1 z1];