Bi level kapurs entropy based image segmentation using particle swarm optimization

Abstract

In the field of Image Processing, Image segmentation is a low level but important task in entire image understanding system which divides an image into its multiple disjoint regions based on homogeneity. In most of the machine vesion and high level image understanding application this is one of the important steps.Till date different techniques of image segmentation are available and hence There exists a huge survey literature in different approaches of Image Segmentation. Selection of image segmentation technique is highly problem specific.There is no versatile algorithm which is applicable for all kinds of images. Optimization based image segmentation is not explored much which can be applied to reduce complexity of the problem. The aim of the paper is to search for an optimized threshold value for Image Segmentation using Particle Swarm Optimization (PSO) algorithm where fitness function is designed based on entropy of the image.

Convergent heterogeneous particle swarm optimisation algorithm for multilevel image thresholding segmentation

“…To evaluate the MTCHPSO method in terms of viability and applicability for multilevel thresholding segmentation, its performance is compared with three similar methods using a set of famous benchmark images which are well‐known in image processing literature. The GA [7, 20], the HS [22, 51] and the PSO [16, 37–42, 52] are selected among many optimisation algorithms [3, 8, 15, 17–19, 21, 51, 53–55] and for convenient we added multilevel thresholding before their names and call them as MTGA, MTHS and MTPSO, respectively. Images are the same in terms of size ( pixels) and properties [all grey‐level with the same format (.tiff)].…”

“…The PSO is one of population‐based global optimisation algorithms [30–34] was inspired by the social network behaviour between individuals (called as particles) in a flock of birds or a school of fishes which their social network helps them to find the best place of food and accommodation for rest. Application of modified versions of PSO were presented in the literature for the problem of multilevel image thresholding [16, 35–42].…”

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

“…With the constantly emerging of the optimization algorithms, a large number of MT methods based on optimization algorithms follow. Fujun et al [17] put forward an improved adaptive genetic algorithm (IAGA) image segmentation method; this method can adjust control parameters adaptively according to the size of individual fitness and dispersion degree of the population, which keeps the diversity of the population and improves the convergence speed; evolutionary algorithms which are inspired by swarm behavior such as Particle Swarm Optimization (PSO) [18] and artificial colony algorithm (ABC) [19] are also widely used in image segmentation problem. Oliva et al [20] used EMO algorithm for MT problem and also applied HAS algorithm [17] to MT tasks; there are many other optimization algorithms which are also used to deal with this kind of problem and the results are also satisfactory, such as DE, CS, BF, and FFA [21–25].…”

Parçacık Sürü Zekası Optimizasyonu ile Mikroskobik Görüntülerin Segmentasyonunda Farklı Entropi Ölçülerinin Etkisi

“…Ek olarak literatürde görüntü bölütleyen optimum eşik degerini bulmak için entropi tabanlı sürü zekası algoritmaları kullanılmıştır. Banerjee ve ark [9]. çalışmalarında PSO algoritması kullanarak görüntü segmentasyonu için optimum eşik degerini bulmayı amaçlamış ve uygunluk fonksiyonu olarak Kapur entropi ölçütünü kullanmışlardır.…”