how can I find regions with more entropy in image?

I want to find regions in image that are more irregular or more messy for example in lena image the hair of lena is irregular for me, now I want features that can show these regions?do you have any idea? thanks in advance.

Answer 1

[Image Analyst]

Try entropyfilt() or stdfilt().

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;

%===============================================================================
% Get the name of the demo image the user wants to use.
% Let's let the user select from a list of all the demo images that ship with the Image Processing Toolbox.
folder = fileparts(which('cameraman.tif')); % Determine where demo folder is (works with all versions).
% Demo images have extensions of TIF, PNG, and JPG.  Get a list of all of them.
imageFiles = [dir(fullfile(folder,'*.TIF')); dir(fullfile(folder,'*.PNG')); dir(fullfile(folder,'*.jpg'))];
for k = 1 : length(imageFiles)
%   fprintf('%d: %s\n', k, files(k).name);
  [~, baseFileName, extension] = fileparts(imageFiles(k).name);
  ca{k} = [baseFileName, extension];
end
% Sort the base file names alphabetically.
[ca, sortOrder] = sort(ca);
imageFiles = imageFiles(sortOrder);
button = menu('Use which gray scale demo image?', ca); % Display all image file names in a popup menu.
% Get the base filename.
baseFileName = imageFiles(button).name; % Assign the one on the button that they clicked on.
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);

%===============================================================================
% Read in a gray scale demo image.
grayImage = imread(fullFileName);
% Get the dimensions of the image.  
% numberOfColorBands should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage);
if numberOfColorChannels > 1
  % It's not really gray scale like we expected - it's color.
  % Convert it to gray scale by taking only the green channel,
  % which in a typical snapshot will be the least noisy channel.
  grayImage = grayImage(:, :, 2); % Take green channel.
end
% Display the image.
subplot(2, 2, 1);
imshow(grayImage, []);
axis on;
axis image;
title('Original Image', 'FontSize', fontSize, 'Interpreter', 'None');

% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0 1 1]);
% Get rid of tool bar and pulldown menus that are along top of figure.
set(gcf, 'Toolbar', 'none', 'Menu', 'none');
% Give a name to the title bar.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off') 

% Get the standard deviation in a sliding window.
stdImage = stdfilt(grayImage);
% Display the image.
subplot(2, 2, 2);
imshow(stdImage, []);
axis on;
axis image;
title('Standard Deviation Image', 'FontSize', fontSize, 'Interpreter', 'None');

entropyImage = entropyfilt(grayImage);
% Display the image.
subplot(2, 2, 3);
imshow(entropyImage, []);
axis on;
axis image;
title('Entropy Image', 'FontSize', fontSize, 'Interpreter', 'None');