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Feature Extraction from Breast ROI

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Warid Islam
Warid Islam 2020 年 6 月 8 日
コメント済み: Warid Islam 2020 年 6 月 9 日
Hello,
I am trying to extract features from breast images using the code below:
clc;clear;close all
%% Getting Image
i=imread('B16.jpg');
figure(1)
imshow(i);title('Original Photo')
% if image is rgb
try
i=rgb2gray(i);
end
%% Crop The Breast
z=im2bw(i,0.1);
figure(2)
imshow(z);title('Original B&W')
info=regionprops(z);
a=cat(1,info.Area);
[m,l]=max(a);
X=info(l).Centroid;
bw2=bwselect(z,X(1),X(2),8);
i=immultiply(i,bw2);
figure(3)
imshow(i);
title('Getting the Breast and Muscle')
%% Deleting Black Ground
% We will delete the black corners
% So that we can select the muscle
% using bwselect
% convert to B&W first time
[x,y]=size(z);
tst1=zeros(x,y);
% detect empty rows
r1=[];
m=1;
for j=1:x
if z(j,:)==tst1(j,:)
r1(m)=j;
m=m+1;
end
end
% detect empty columns
r2=[];
m=1;
for j=1:y
if z(:,j)==tst1(:,j)
r2(m)=j;
m=m+1;
end
end
% Deleting
i(:,r2)=[];
i(r1,:)=[];
figure(4)
imshow(i);title('after deleting background');
%% Deleting the Muscle
if i(1,1)~=0
c=3;
r=3;
else
r=3;
c=size(i,2)-3;
end
z2=im2bw(i,0.5);
bw3=bwselect(z2,c,r,8);
bw3=~bw3;
ratio=min(sum(bw3)/sum(z2));
if ratio>=1
i=immultiply(i,bw3);
else
z2=im2bw(i,0.75);
bw3=bwselect(z2,c,r,8);
ratio2=min(sum(bw3)/sum(z2));
if round(ratio2)==0
lvl=graythresh(i);
z2=im2bw(i,1.75*lvl);
bw3=bwselect(z2,c,r,8);
bw3=~bw3;
i=immultiply(i,bw3);
else
bw3=~bw3;
i=immultiply(i,bw3);
end
end
figure(5)
imshow(i)
title('Getting only the Breast')
%% Adaptive Median Filter
% clc;
% clear;
% close all;
% a=imread('M1.jpg');
% b=rgb2gray(i);
J = imnoise(i,'salt & pepper', 0.02);
NoisyImage=J;
[R C P]=size(NoisyImage);
OutImage=zeros(R,C);
figure;
% imshow(J);
Zmin=[];
Zmax=[];
Zmed=[];
for i=1:R
for j=1:C
if (i==1 & j==1)
% for right top corner[8,7,6]
elseif (i==1 & j==C)
% for bottom left corner[2,3,4]
elseif (i==R & j==1)
% for bottom right corner[8,1,2]
elseif (i==R & j==C)
%for top edge[8,7,6,5,4]
elseif (i==1)
% for right edge[2,1,8,7,6]
elseif (i==R)
% // for bottom edge[8,1,2,3,4]
elseif (j==C)
%// for left edge[2,3,4,5,6]
elseif (j==1)
else
SR1 = NoisyImage((i-1),(j-1));
SR2 = NoisyImage((i-1),(j));
SR3 = NoisyImage((i-1),(j+1));
SR4 = NoisyImage((i),(j-1));
SR5 = NoisyImage(i,j);
SR6 = NoisyImage((i),(j+1));
SR7 = NoisyImage((i+1),(j-1));
SR8 = NoisyImage((i+1),(j));
SR9 = NoisyImage((i+1)),((j+1));
TempPixel=[SR1,SR2,SR3,SR4,SR5,SR6,SR7,SR8,SR9];
Zxy=NoisyImage(i,j);
Zmin=min(TempPixel);
Zmax=max(TempPixel);
Zmed=median(TempPixel);
A1 = Zmed - Zmin;
A2 = Zmed - Zmax;
if A1 > 0 && A2 < 0
% go to level B
B1 = Zxy - Zmin;
B2 = Zxy - Zmax;
if B1 > 0 && B2 < 0
OutImage(i,j)= Zxy;
else
OutImage(i,j)= Zmed;
end
else
if ((R > 4 && R < R-5) && (C > 4 && C < C-5))
S1 = NoisyImage((i-1),(j-1));
S2 = NoisyImage((i-2),(j-2));
S3 = NoisyImage((i-1),(j));
S4 = NoisyImage((i-2),(j));
S5 = NoisyImage((i-1),(j+1));
S6 = NoisyImage((i-2),(j+2));
S7 = NoisyImage((i),(j-1));
S8 = NoisyImage((i),(j-2));
S9 = NoisyImage(i,j);
S10 = NoisyImage((i),(j+1));
S11 = NoisyImage((i),(j+2));
S12 = NoisyImage((i+1),(j-1));
S13 = NoisyImage((i+2),(j-2));
S14 = NoisyImage((i+1),(j));
S15 = NoisyImage((i+2),(j));
S16 = NoisyImage((i+1)),((j+1));
S17 = NoisyImage((i+2)),((j+2));
TempPixel2=[S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11,S12,S13,S14,S15,S16,S17];
Zmed2=median(TempPixel2);
OutImage(i,j)= Zmed2;
else
OutImage(i,j)= Zmed;
end
end
end
end
end
imshow(OutImage,[]);
title('Adaptive Median Filter')
disp('exit');
%%GLCM Feature Extraction
% Y=rgb2gray(OutImage);
Y=double(OutImage);
% Y=OutImage;
% ShapeTexture=wlt4(Y);
% statsa = GLCM_Features4(Y,ShapeTexture);
% ExtractedFeatures1=statsa;
% glcm2=graycomatrix(Y,'Offset',[2 0;0 2]);
% glcm2=graycomatrix(Y);
% stats = GLCM_Features1(glcm2,0);
% ExtractedFeatures1=stats;
% statsTable = struct2table(stats);
% statsArray = table2array(statsTable);
% statsArray'
k=2; % k: number of regions
g=2; % g: number of GMM components
beta=1; % beta: unitary vs. pairwise
EM_iter=10; % max num of iterations
MAP_iter=10; % max num of iterations
[X,GMM,ShapeTexture]=image_kmeans(Y,k,g);
[X,Y,GMM]=HMRF_EM(X,Y,GMM,k,g,EM_iter,MAP_iter,beta);
Y=Y*80;
Y=uint8(Y);
Y =double(Y);
statsa = glcm(Y,0,ShapeTexture);
ExtractedFeatures1=statsa;
imshow(Y,[]);
However, I am getting the following error:
Error using reshape
To RESHAPE the number of elements must not change.
Error in MRF_MAP (line 10)
y=reshape(Y,[m*n 3]);
Error in HMRF_EM (line 8)
[X sum_U(it)]=MRF_MAP(X,Y,GMM,k,g,MAP_iter,beta,0);
Error in weiner (line 275)
[X,Y,GMM]=HMRF_EM(X,Y,GMM,k,g,EM_iter,MAP_iter,beta);
I have attached the relevant files above. Any help would be very much appreciated. Thank you.

採用された回答

Image Analyst
Image Analyst 2020 年 6 月 8 日
Evidently Y does not have the same number of elements. Put this right before the error and what does it show in the command window.
whos Y
m
n
numberOfPixels = numel(Y)
mn3 = m * n * 3
Don't use semicolons. Tell us what you see. You'll see that numberOfPixels does not equal mn3 and since they're not the same, some pixels won't get used, or you're missing some pixels. Either way, a reshape cannot happen.
  7 件のコメント
Warid Islam
Warid Islam 2020 年 6 月 9 日
Hi @Image Analyst,
I tried to implement the GMM-Based Hidden Markov Random Field from the following link:
But, I guess I followed the wrong process as it is for color segmentation and my image is grayscale.

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