how to get better preprocessing results

Question

Walaa 2022-12-10

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1875247-how-to-get-better-preprocessing-results

回答： Image Analyst 2022-12-10

I'm using the steps in the code below as preprocessing steps before cup and disc segmentation of a retinal image. any advices for better results?

these are the results that I get:

InputImage=imread('18.png');
%preprocessing  
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.97]);
% Give a name to the title bar.
set(gcf,'name','Preprocessing for optic Cup extraction','numbertitle','off') 
subplot(2,3,1)
imshow(InputImage)
axis on;
title('Original Image')
%-------------------------------------------------------------------------------------------------------------------------------------------
%segementation and removal of blood vessels
IMAGE = im2double(InputImage);
% Convert RGB to Gray via PCA
lab = rgb2lab(IMAGE);
f = 0;
wlab = reshape(bsxfun(@times,cat(3,1-f,f/2,f/2),lab),[],3);
[C,S] = pca(wlab);
S = reshape(S,size(lab));
S = S(:,:,1);
gray = (S-min(S(:)))./(max(S(:))-min(S(:)));
%% Contrast Enhancment of gray image using CLAHE
J = adapthisteq(gray,'numTiles',[8 8],'nBins',128);
%% Background Exclusion
% Apply Average Filter
h = fspecial('average', [9 9]);
JF = imfilter(J, h);
% Take the difference between the gray image and Average Filter
Z = imsubtract(JF, J);
%% Threshold using the IsoData Method
level=isodata(Z); % this is our threshold level
%level = graythresh(Z)
%% Convert to Binary
BW = imbinarize(Z, level-.003);
%% Remove small pixels
BW2 = bwareaopen(BW, 350);
%% Overlay
BW2 = imcomplement(BW2);
out = imoverlay(InputImage, BW2, [0 0 0]);
subplot(2,3,2);
imshow(out);
axis on;
title('Segmented blood vessels');
mask=rgb2gray(out);
mask=logical(mask);
%Inpaint the original image by removing the text overlays.
J = inpaintCoherent(InputImage,mask);
subplot(2,3,3);
imshow(J);
axis on;
title('Removed blood vessels using inpaint coherent');
%Display the original image and the inpainted image.
y= inpaintExemplar(J,mask,'FillOrder','tensor','PatchSize',18);
subplot(2,3,4)
imshow(y);
axis on;
title('Further removal of blood vessels using inpaint exemplar')
%---------------------------------------------------------------------------------------------------------------------------------------------
%noise removal&smoothing of the retinal image
rgbImage = y;
% Get the dimensions of the image.  numberOfColorBands should be = 3.
[rows, ~, numberOfColorBands] = size(rgbImage);
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
% Median Filter the channels:
redMF = medfilt2(redChannel, [5, 5]);
greenMF = medfilt2(greenChannel, [5, 5]);
blueMF = medfilt2(blueChannel, [5, 5]);
% Find the noise in the red.
noiseImage = redChannel > 10;
% Get rid of the noise in the red by replacing with median.
noiseFreeRed = redChannel;
noiseFreeRed(noiseImage) = redMF(noiseImage);
% Find the noise in the green.
noiseImage = greenChannel > 20;
% Get rid of the noise in the green by replacing with median.
noiseFreeGreen = greenChannel;
noiseFreeGreen(noiseImage) = greenMF(noiseImage);
% Find the noise in the blue.
noiseImage = blueChannel > 100;
% Get rid of the noise in the blue by replacing with median.
noiseFreeBlue = blueChannel;
noiseFreeBlue(noiseImage) = blueMF(noiseImage);
% Reconstruct the noise free RGB image
rgbFixed = cat(3, noiseFreeRed, noiseFreeGreen, noiseFreeBlue);
subplot(2, 3,5);
imshow(rgbFixed);
axis on;
impixelinfo; % Allow user to mouse around and see the pixel values and coordinates.
title('Smoothed image');
%--------------------------------------------------------------------------------------------------------------------------------------------
%enhance contrast
shadow = rgbFixed;
shadow_lab = rgb2lab(shadow);
%The values of luminosity span a range from 0 to 100. Scale the values to the range [0 1], which is the expected range of images with data type double.
max_luminosity = 100;
L = shadow_lab(:,:,1)/max_luminosity;
%Perform the three types of contrast adjustment on the luminosity channel, and keep the a* and b* channels unchanged. Convert the images back to the RGB color space.
shadow_imadjust = shadow_lab;
shadow_imadjust(:,:,1) = imadjust(L)*max_luminosity;
shadow_imadjust = lab2rgb(shadow_imadjust);
subplot(2,3,6);
imshow(shadow_imadjust);
axis on;
title('Enhanced contrast');
uiwait(helpdlg('Examine the figures, then click OK to finish.'));
InputImage=imread(inp1);
%preprocessing  
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.97]);
% Give a name to the title bar.
set(gcf,'name','Preprocessing for optic Cup extraction','numbertitle','off') 
subplot(2,3,1)
imshow(InputImage)
axis on;
title('Original Image')
%-------------------------------------------------------------------------------------------------------------------------------------------
%segementation and removal of blood vessels
IMAGE = im2double(InputImage);
% Convert RGB to Gray via PCA
lab = rgb2lab(IMAGE);
f = 0;
wlab = reshape(bsxfun(@times,cat(3,1-f,f/2,f/2),lab),[],3);
[C,S] = pca(wlab);
S = reshape(S,size(lab));
S = S(:,:,1);
gray = (S-min(S(:)))./(max(S(:))-min(S(:)));
%% Contrast Enhancment of gray image using CLAHE
J = adapthisteq(gray,'numTiles',[8 8],'nBins',128);
%% Background Exclusion
% Apply Average Filter
h = fspecial('average', [9 9]);
JF = imfilter(J, h);
% Take the difference between the gray image and Average Filter
Z = imsubtract(JF, J);
%% Threshold using the IsoData Method
level=isodata(Z); % this is our threshold level
%level = graythresh(Z)
%% Convert to Binary
BW = imbinarize(Z, level-.003);
%% Remove small pixels
BW2 = bwareaopen(BW, 350);
%% Overlay
BW2 = imcomplement(BW2);
out = imoverlay(InputImage, BW2, [0 0 0]);
subplot(2,3,2);
imshow(out);
axis on;
title('Segmented blood vessels');
mask=rgb2gray(out);
mask=logical(mask);
%Inpaint the original image by removing the text overlays.
J = inpaintCoherent(InputImage,mask);
subplot(2,3,3);
imshow(J);
axis on;
title('Removed blood vessels using inpaint coherent');
%Display the original image and the inpainted image.
y= inpaintExemplar(J,mask,'FillOrder','tensor','PatchSize',18);
subplot(2,3,4)
imshow(y);
axis on;
title('Further removal of blood vessels using inpaint exemplar')
%---------------------------------------------------------------------------------------------------------------------------------------------
%noise removal&smoothing of the retinal image
rgbImage = y;
% Get the dimensions of the image.  numberOfColorBands should be = 3.
[rows, ~, numberOfColorBands] = size(rgbImage);
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
% Median Filter the channels:
redMF = medfilt2(redChannel, [5, 5]);
greenMF = medfilt2(greenChannel, [5, 5]);
blueMF = medfilt2(blueChannel, [5, 5]);
% Find the noise in the red.
noiseImage = redChannel > 10;
% Get rid of the noise in the red by replacing with median.
noiseFreeRed = redChannel;
noiseFreeRed(noiseImage) = redMF(noiseImage);
% Find the noise in the green.
noiseImage = greenChannel > 20;
% Get rid of the noise in the green by replacing with median.
noiseFreeGreen = greenChannel;
noiseFreeGreen(noiseImage) = greenMF(noiseImage);
% Find the noise in the blue.
noiseImage = blueChannel > 100;
% Get rid of the noise in the blue by replacing with median.
noiseFreeBlue = blueChannel;
noiseFreeBlue(noiseImage) = blueMF(noiseImage);
% Reconstruct the noise free RGB image
rgbFixed = cat(3, noiseFreeRed, noiseFreeGreen, noiseFreeBlue);
subplot(2, 3,5);
imshow(rgbFixed);
axis on;
impixelinfo; % Allow user to mouse around and see the pixel values and coordinates.
title('Smoothed image');
%--------------------------------------------------------------------------------------------------------------------------------------------
%enhance contrast
shadow = rgbFixed;
shadow_lab = rgb2lab(shadow);
%The values of luminosity span a range from 0 to 100. Scale the values to the range [0 1], which is the expected range of images with data type double.
max_luminosity = 100;
L = shadow_lab(:,:,1)/max_luminosity;
%Perform the three types of contrast adjustment on the luminosity channel, and keep the a* and b* channels unchanged. Convert the images back to the RGB color space.
shadow_imadjust = shadow_lab;
shadow_imadjust(:,:,1) = imadjust(L)*max_luminosity;
shadow_imadjust = lab2rgb(shadow_imadjust);
subplot(2,3,6);
imshow(shadow_imadjust);
axis on;
title('Enhanced contrast');
uiwait(helpdlg('Examine the figures, then click OK to finish.'));