EVALUATING THE PERFORMANCE OF IMAGE SEGMENTATION METHODS: RANDOM WAKJER METHOD, MUMFORD SHAH METHOD, MORPHOLOGICAL TRANSFORMATIONS METHOD AND K-MEANS CLUSTERING METHOD

Abstract

This paper explores synergistic integration of the semi-automatic segmentation techniques, im proved the K-Means clustering algorithms, the morphological transformations, and the Mumford-Shah (MS) functional approximation methods in fields of an image processing and computational geome try. The Semi-automatic segmentation, facilitated by an user interaction, enables precise delineation of the regions of interest within images, offering versatility across the diverse applications. A Ran dom Walker method, is known for its flexibility in segmenting images into the multiple objects and complements traditional binary segmentation approaches. Conversely, MS functional, renowned for modelling images as the piecewise-smooth functions, has seen limited adoption in the geometry pro cessing due to the computational complexities and challenges in a mesh adaptation. To address these issues, advancements have merged algorithms such as the largest minimum distance algorithm with the traditional K-Means clustering and enhancing cluster analysis efficiency. Moreover, an integration of the morphological transformations with a MS functional approximation methods facilitates the noise reduction, an edge detection and the boundary extraction in images. This paper investigates fusion of these methodologies to solve challenges in an image and the geometry processing, offering insights into their applications, the potential advancements in the computational image and geometry processing.