A study and comparison of different image segmentation algorithms

Citations Over TimeTop 12% of 2016 papers

Abstract

Image segmentation is a process, which divide an image into different regions, which are homogeneous in some characteristics. Image segmentation is the initial step in many image processing applications like Pattern recognition and image analysis. Image analysis includes object characterization and representation and feature measurement. Higher order task follows the classification of object. Hence, characterization, visualization of region of interest in any image, delineation plays a significant role in image segmentation. There are many segmentation algorithms available in the literature, which divide an image into number of regions based on some image features like pixel intensity value, color, texture, etc. These all algorithms are categorized based on the segmentation method used. They are Segmentation based on single or multiple thresh holding, Segmentation based on edge detection, Segmentation based on similar region and Segmentation based on clustering, Segmentation based on ANN and fuzzy logic technique etc. In this work, we have chose one algorithm from one segmentation category and implement the algorithm in MATLAB. The chosen algorithms are Otsu's algorithm, K-means, quad tree, Delta E, Region growing and fth algorithms. To check the performance of the algorithms, we have used 6 simple and complex images available in the literature. The obtained results shows the efficacy of the segmentation algorithms.

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