Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

Abstract

Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering based algorithms were developed as multidimensional extensions of thresholding. While these algorithms have demonstrated successful results for fewer thresholds, their computational costs for a large number of thresholds are still a limiting factor. We propose a new clustering algorithm based on linear partitioning of the pixel intensity set and between-cluster variance criterion function for multilevel image segmentation. The results of testing the proposed algorithm on real images from Berkeley Segmentation Dataset and Benchmark show that the algorithm is comparable with state-of-the-art multilevel segmentation algorithms and consistently produces high quality results. The attractive properties of the algorithm are its simplicity, generalization to a large number of clusters, and computational cost effectiveness.

Multi-Level Image Thresholding via Nonlinear Fitting of the Histogram

“…To save the effort in reproducing results of other schemes, the results of the proposed scheme where compared to that of Olugbara et al (2015). where an extensive comparison is made with some meta-heuristic schemes following the between class variance optimization originally proposed by Otsu (1979).…”

“…where an extensive comparison is made with some meta-heuristic schemes following the between class variance optimization originally proposed by Otsu (1979). Olugbara et al (2015). claim the improved performance of their scheme.…”

“…To overcome the burden of exhaustive search, many researchers have proposed the use of metaheuristic schemes as in Oliva et al (2018;Hu et al, 2019). These schemes, including the biologically inspired algorithms, may not be the best solutions as demonstrated by Balabanian et al (2017;Merzban and Elbayoumi, 2019;Olugbara et al, 2015).…”

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

“…Edge based methods such as zero-crossing of Laplacian-of-Gaussian [27] and geodesic active contour [28] are aimed at detecting discontinuities in image pixel intensity values [29]. Pixel based methods group similar pixels as belonging to a homogenous cluster that corresponds to an object or part of an object [30] and are widely applied because of their inherent simplicity and robustness [31,32]. Thresholding and clustering algorithms are archetypes of the pixel based methods that have been applied for segmentation of skin lesion [9,33].…”

A Novel MRI Image Segmentation Algorithm based on Modified Neural Network Model

“…The algorithm in the field of the image processing and image recognition has made remarkable achievements, its main Copyright ⓒ 2016 SERSC idea is to use known to determine the model of network training samples, and then using the trained network for image processing and recognition [24][25][26].…”