MATLAB Code Implementation for Image Binarization and Morphological Operations

In digital image processing, the following basic algorithm programs can be implemented to process images: 1. Image Binarization: Converting images into pixels with only black and white colors. Implementation typically involves thresholding using functions like im2bw() or graythresh() to determine optimal separation values. 2. Image Dilation: Expanding pixels in the image to make objects larger. This morphological operation can be implemented using imdilate() function with structuring elements like strel('disk', radius). 3. Image Erosion: Shrinking pixels in the image to make objects smaller. Achieved through imerode() function with appropriate structuring elements to remove boundary pixels. 4. Image Opening Operation: Performing erosion followed by dilation to remove small-sized noise from images. This can be implemented sequentially or using imopen() function directly with morphological structuring elements. 5. Image Closing Operation: Performing dilation followed by erosion to fill small holes in images. Implemented through sequential operations or imclose() function to connect broken segments. 6. Image Thinning Processing: Making lines in the image thinner through iterative operations. Algorithms like Zhang-Suen or Guo-Hall thinning can be implemented using bwmorph() function with 'thin' parameter. These fundamental digital image processing algorithms can be efficiently implemented using MATLAB's advanced programming language and Image Processing Toolbox functions for precise morphological operations.