Can I get automatic Nail Image segmentation code?

when input hand image is given,the output must contain segmented nail image

[Dyuman Joshi]

"when input hand image is given,the output must contain segmented nail image"

Okay!

On a serious note - This seems like a HW assignment, given the exact same question has been asked just a few minutes ago - https://in.mathworks.com/matlabcentral/answers/2065571-i-want-to-nails-semantic-segmentation-using-the-nail-image-from-fingers

Show us what you have tried yet.

[Yahya]

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clearvars;
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 16;
fprintf('Beginning to run %s.m ...\n', mfilename);
%-----------------------------------------------------------------------------------------------------------------------------------
% Read in image.
folder = [];
baseFileName = 'leaf1.jpeg';
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
    % The file doesn't exist -- didn't find it there in that folder.
    % Check the entire search path (other folders) for the file by stripping off the folder.
    fullFileNameOnSearchPath = baseFileName; % No path this time.
    if ~exist(fullFileNameOnSearchPath, 'file')
        % Still didn't find it.  Alert user.
        errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
        uiwait(warndlg(errorMessage));
        return;
    end
end
% It's not an RGB image!  It's an indexed image, so read in the indexed image...
rgbImage = imread(fullFileName);
[rows, columns, numberOfColorChannels] = size(rgbImage)
% Display the test image.
subplot(2, 2, 1);
imshow(rgbImage, []);
axis('on', 'image');
caption = sprintf('Image : "%s"', baseFileName);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Set up figure properties:
% Enlarge figure to full screen.
hFig1 = gcf;
hFig1.Units = 'Normalized';
hFig1.WindowState = 'maximized';
% 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.
hFig1.Name = 'Demo by Image Analyst';
[mask, maskedRGBImage] = createMask(rgbImage);
% Take just the largest regions:
mask = bwareafilt(mask, 1);
% Fill Holed.
mask = imfill(mask, 'holes');
% Display the initial mask image.
subplot(2, 2, 2);
imshow(mask, []);
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
axis('on', 'image');
title('Mask', 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
% Mask the image using bsxfun() function to multiply the mask by each channel individually.  Works for gray scale as well as RGB Color images.
maskedRgbImage = bsxfun(@times, rgbImage, cast(mask, 'like', rgbImage));
% Display the final masked image.
subplot(2, 2, 3);
imshow(maskedRgbImage, []);
axis('on', 'image');
title('Masked Image', 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Display the final masked image of the background by inverting the mask.
% Mask the image using bsxfun() function to multiply the mask by each channel individually.  Works for gray scale as well as RGB Color images.
backgroundImage = bsxfun(@times, rgbImage, cast(~mask, 'like', rgbImage));
subplot(2, 2, 4);
imshow(backgroundImage, []);
axis('on', 'image');
title('Background Image', 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
%-------------------------------------------------------------------------------------------------------------
% Make measurements
props = regionprops(mask, 'Area', 'Centroid')
allAreas = [props.Area];
xyCentroids = vertcat(props.Centroid);
subplot(2, 2, 2);
hold on;
for k = 1 : length(props)
    x = xyCentroids(k, 1);
    y = xyCentroids(k, 2);
    txt = sprintf('  (x, y) = (%.1f, %.1f).  Area = %d', ...
        x, y, allAreas(k));
    text(x, y, txt, 'Color', 'r', 'FontWeight', 'bold');
    plot(x, y, 'r+', 'MarkerSize', 25, 'LineWidth', 2);
end
% Get boundary.
boundaries = bwboundaries(mask);
boundaries = boundaries{1}; % Extract from cell.
x = boundaries(:, 2);
y = boundaries(:, 1);
plot(x, y, 'r-', 'LineWidth', 3);
fprintf('Done running %s.m ...\n', mfilename);
msgbox('Done!');
function [BW,maskedRGBImage] = createMask(RGB)
%createMask  Threshold RGB image using auto-generated code from colorThresholder app.
%  [BW,MASKEDRGBIMAGE] = createMask(RGB) thresholds image RGB using
%  auto-generated code from the colorThresholder app. The colorspace and
%  range for each channel of the colorspace were set within the app. The
%  segmentation mask is returned in BW, and a composite of the mask and
%  original RGB images is returned in maskedRGBImage.
% Auto-generated by colorThresholder app on 01-Nov-2020
%------------------------------------------------------
% Convert RGB image to chosen color space
I = rgb2hsv(RGB);
% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.183;
channel1Max = 0.400;
% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.000;
channel2Max = 1.000;
% Define thresholds for channel 3 based on histogram settings
channel3Min = 0.000;
channel3Max = 1.000;
% Create mask based on chosen histogram thresholds
sliderBW = (I(:,:,1) >= channel1Min ) & (I(:,:,1) <= channel1Max) & ...
    (I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
    (I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);
BW = sliderBW;
% Initialize output masked image based on input image.
maskedRGBImage = RGB;
% Set background pixels where BW is false to zero.
maskedRGBImage(repmat(~BW,[1 1 3])) = 0;
end

we tried to study this code and implement in our code but it did not work. so can you provide any guidance

[Image Analyst]

Isn't this a duplicate post to https://www.mathworks.com/matlabcentral/answers/2065571-i-want-to-nails-semantic-segmentation-using-the-nail-image-from-fingers#comment_3014971?

Are you and Mithun the same person or just in the same class?

[Yahya]

We are different person working on same project

Answer 1

[Ayush]

Hi @Yahya

I understand that you need an automatic Nail Image segmentation code which takes hand image as an input, to produce segmented nail image. Here is the conceptual code for that:

function segmented_nails = segment_nails(image_path)
    % Read the image
    hand_image = imread(image_path);
    
    % Convert the image to YCbCr color space
    YCbCr_img = rgb2ycbcr(hand_image);
    Cb = YCbCr_img(:,:,2);
    Cr = YCbCr_img(:,:,3);
    
    % These thresholds are just starting points and may require fine-tuning
    cb_min = 100; % Lower Cb threshold (adjust as needed)
    cb_max = 140; % Upper Cb threshold (adjust as needed)
    cr_min = 140; % Lower Cr threshold (adjust as needed)
    cr_max = 175; % Upper Cr threshold (adjust as needed)
    
    % Create a binary mask based on the adjusted thresholds
    binary_mask = (Cb >= cb_min) & (Cb <= cb_max) & (Cr >= cr_min) & (Cr <= cr_max);
    
    % Morphological operations to clean up the segmentation
    binary_mask = imfill(binary_mask, 'holes');
    binary_mask = bwareaopen(binary_mask, 50); % Remove small objects
    binary_mask = imdilate(binary_mask, strel('disk', 5));
    
    % Extract the segmented nails
    segmented_nails = hand_image;
    for i = 1:3
        channel = segmented_nails(:,:,i);
        channel(binary_mask == 0) = 0;
        segmented_nails(:,:,i) = channel;
    end
    
    % Display the original and segmented images
    subplot(1, 2, 1);
    imshow(hand_image);
    title('Original Image');
    
    subplot(1, 2, 2);
    imshow(segmented_nails);
    title('Segmented Nails');
end

Note that even with adjustment, simple color-based thresholding is a heuristic approach and may not work perfectly in all cases. For more robust segmentation, you might consider training a machine learning model, such as a convolutional neural network (CNN), on a dataset of hand images with labeled nails.

Thanks,

Ayush

[Yahya]

Thank You for the guidance sir, but still the output is not proper.Is there any possibility for guiding in training machine learning model

[Ayush]

You may need your dataset for train such a model. However, I can help you with the conceptual code for creating and training a CNN model.

% Define the network input size and number of classes
inputSize = [256, 256, 3]; % Example input size (height, width, channels)
numClasses = 2; % Example number of classes (e.g., nail, background)

% Create the CNN for segmentation
% This function is defined in below code snippet
lgraph = createNailSegmentationCNN(inputSize, numClasses);

% visualize the network
analyzeNetwork(lgraph)


% Load your dataset (assuming imageDatastore and pixelLabelDatastore are prepared)
imageDir = 'path/to/images';
labelDir = 'path/to/labels';

% Create an imageDatastore for the images
imds = imageDatastore(imageDir);

% Create a pixelLabelDatastore for the labels
classNames = ["background", "nail"]; % Define class names as per your dataset
labelIDs = [0, 255]; % Define label IDs as per your dataset
pxds = pixelLabelDatastore(labelDir, classNames, labelIDs);

% Define training options
options = trainingOptions('sgdm', ...
    'InitialLearnRate', 1e-3, ...
    'MaxEpochs', 20, ...
    'MiniBatchSize', 8, ...
    'Shuffle', 'every-epoch', ...
    'VerboseFrequency', 2, ...
    'Plots', 'training-progress');

% Train the network
[net, trainInfo] = trainNetwork(imds, pxds, lgraph, options);

% After training, use the trained network to segment new images
newImage = imread('path/to/new/image.jpg');
C = semanticseg(newImage, net);

% Visualize the segmentation result
B = labeloverlay(newImage, C, 'Colormap', [0 1 0; 1 0 0], 'Transparency',0.4);
figure, imshow(B), title('Segmented Image');

Function to create the CNN:

function lgraph = createNailSegmentationCNN(inputSize, numClasses)
    % Define the layers of the network
    layers = [
        imageInputLayer(inputSize, 'Name', 'input', 'Normalization', 'none')
        
        convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv1_1')
        reluLayer('Name', 'relu1_1')
        convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv1_2')
        reluLayer('Name', 'relu1_2')
        maxPooling2dLayer(2, 'Stride', 2, 'Name', 'pool1')
        
        convolution2dLayer(3, 128, 'Padding', 'same', 'Name', 'conv2_1')
        reluLayer('Name', 'relu2_1')
        convolution2dLayer(3, 128, 'Padding', 'same', 'Name', 'conv2_2')
        reluLayer('Name', 'relu2_2')
        maxPooling2dLayer(2, 'Stride', 2, 'Name', 'pool2')
        
        % Add more layers as needed for your application
        
        % Final layers for segmentation
        convolution2dLayer(1, numClasses, 'Padding', 'same', 'Name', 'convFinal')
        softmaxLayer('Name', 'softmax')
        pixelClassificationLayer('Name', 'pixelClassification')
    ];

    % Create a layer graph from the layer array
    lgraph = layerGraph(layers);
end

Thanks,

Ayush