MATLAB Image Segmentation: Techniques and Implementation

Image segmentation refers to the process of partitioning digital images into segments (sets of pixels, also known as superpixels). The primary objective of segmentation is to simplify and/or transform image representation to make it more meaningful and easier to analyze [1][2]. This technique is commonly employed to locate objects and boundaries (lines, curves, etc.) within images. More precisely, image segmentation involves assigning labels to each pixel in an image so that pixels sharing identical labels possess common characteristics. In MATLAB implementations, common segmentation methods include: threshold-based techniques using functions like imbinarize; region-based approaches such as watershed transformation with watershed function; and clustering methods like k-means segmentation using imsegkmeans. These algorithms typically handle preprocessing steps including noise reduction with filters (imgaussfilt) and contrast enhancement (imadjust) before actual segmentation. Image segmentation finds extensive applications in medical image analysis, computer vision, and pattern recognition domains.