how to batch process TIFF files in MATLAB using the Image Processing Toolbox, with code and explanations:

Here’s a structured approach to batch process TIFF files in MATLAB using the Image Processing Toolbox, with code and explanations:

Solution Code

% Batch process TIFF files (e.g., denoising, segmentation, enhancement)
inputDir = 'path/to/input_folder';    % Folder containing input TIFFs
outputDir = 'path/to/output_folder';  % Folder to save processed TIFFs

% Create output directory if it doesn't exist
if ~exist(outputDir, 'dir')
    mkdir(outputDir);
end

% Get list of TIFF files
tiffFiles = dir(fullfile(inputDir, '*.tif*')); % Includes .tif and .tiff

% Loop through all files
for i = 1:length(tiffFiles)
    % Read the TIFF file
    filename = tiffFiles(i).name;
    [~, baseName, ext] = fileparts(filename);
    img = imread(fullfile(inputDir, filename));
    
    % Process the image (customize this step)
    % Example 1: Denoise using median filtering
    processedImg = medfilt2(img, [3 3]);
    
    % Example 2: Adaptive thresholding for segmentation
    % processedImg = imbinarize(img, 'adaptive');
    
    % Example 3: Contrast adjustment
    % processedImg = imadjust(img);
    
    % Save the processed image
    outputFilename = fullfile(outputDir, ['processed_', baseName, ext]);
    imwrite(processedImg, outputFilename, 'TIFF');
    
    fprintf('Processed: %s\n', filename);
end

Key Steps and Customization

1. File Handling:
   • Input/Output Directories: Replace inputDir and outputDir with your actual paths.
   • Multi-Frame TIFFs: Use imread with a loop for multi-frame TIFFs:
     matlab

     Copy

     info = imfinfo(fullfile(inputDir, filename));
     for k = 1:numel(info)
         img = imread(fullfile(inputDir, filename), k);
         % Process each frame
     end

2. Processing Operations:
   • Denoising: Use medfilt2, imgaussfilt, or wiener2.
   • Segmentation: Apply imbinarize, watershed, or edge detection (edge).
   • Enhancement: Use imadjust, histeq, or adapthisteq.
   • Morphology: Perform operations like imopen, imclose, or imtophat.
3. Parallel Processing (for large datasets):
   matlab

   Copy

   parfor i = 1:length(tiffFiles) % Requires Parallel Computing Toolbox
       % Same processing loop as above
   end

Example: Batch Resize and Convert to Grayscale

for i = 1:length(tiffFiles)
    filename = tiffFiles(i).name;
    img = imread(fullfile(inputDir, filename));
    
    % Resize to 50% and convert to grayscale
    imgResized = imresize(img, 0.5);
    imgGray = rgb2gray(imgResized); % Skip if already grayscale
    
    % Save
    imwrite(imgGray, fullfile(outputDir, ['resized_', filename]));
end

Advanced Workflow

1. Metadata Preservation:
   • Use imfinfo to retain metadata:
     matlab

     Copy

     info = imfinfo(fullfile(inputDir, filename));
     imwrite(processedImg, outputFilename, 'TIFF', 'Resolution', info(1).XResolution);

2. Error Handling:
   • Wrap the loop in a try-catch block to log errors:
     matlab

     Copy

     try
         % Processing code
     catch ME
         fprintf('Error processing %s: %s\n', filename, ME.message);
     end

3. Batch Processing with imageDatastore:
   matlab

   Copy

   imds = imageDatastore(inputDir, 'FileExtensions', {'.tif', '.tiff'});
   while hasdata(imds)
       [img, info] = read(imds);
       processedImg = yourProcessingFunction(img);
       imwrite(processedImg, fullfile(outputDir, ['processed_', info.Filename]));
   end

Notes:

• TIFF Compression: Use imwrite options like 'Compression' (e.g., 'none', 'lzw') for large files.
• Multi-Channel Images: Handle RGB vs. grayscale images using size(img, 3).
• Performance: For large datasets, preallocate arrays or use blockproc for tiled processing.

This framework allows you to automate repetitive tasks like filtering, segmentation, or format conversion across hundreds of TIFF files. Replace the example operations with your specific workflow.