A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Abstract

Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA) with Fuzzy C-Means algorithm (FCM). In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions) MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.

Developing and Implementing a Web-Based Recycling System for Protecting the Green Environment

“…They could be external hardware, human users or other application helps individuals get rid of some recyclable waste in exchange for certain points and allows them to specify the days and hours they want to get rid 32], even the methods of complaint management applications for the real world complaint management issues. Today, the use of Artificial Intelligence (AI) algorithms is expansive, particularly in providing solution to challenging problems including patterns recognition and retrieval of information [27,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49], 55], analysis of medical image [56][57][58][59][60], Learning e Monitoring system [26,91]. Accordingly, many researchers have used the Artificial Intelligence as an effective tool for environment protection, sustainability and Smart process of system analysis aims to study an existing system to entirely design a new system.…”

An Elite Pool-Based Big Bang-Big Crunch Metaheuristic for Data Clustering

“…Thus, within the last 20 years, there have been applications of countless heuristic approaches as an attempt to overcome the problems associated with K-means. Among the approaches used include: Simulated annealing by Güngör and Ünler (2007), tabu search by Liu et al (2008), genetic algorithm by Liu et al (2012), neural gas algorithm by Qin and Suganthan (2004), honey bee mating optimization by Fathian et al (2007), artificial bee colony by Karaboga and Ozturk (2011) and Alsmadi (2015), particle swarm optimization algorithm by Kuo et al (2012), ant colony optimization by Zhang and Cao (2011), differential evolution algorithm by Das et al (2009), gravitational search algorithm by Hatamlou et al (2012), firefly algorithm by Senthilnath et al (2011) and Alsmadi (2014), big bang-big crunch algorithm by Hatamlou et al (2011) and black hole heuristic by Hatamlou (2013); all these approaches have been used for data clustering.…”

“…Meanwhile, the use of the techniques of clustering can be seen in numerous domains including geophysics (Song et al, 2010), agriculture (Chinchuluun et al, 2009), image processing (Alsmadi, 2015;2014;Mitra and Kundu, 2011;Farag et al, 2017;Alsmadi, 2017d), document clustering (Cai and Li, 2011), prediction (Chen and Chang, 2010), security and detection of crime (Grubesic, 2006), marketing and costumer analysis (Li et al, 2009), anomaly detection (Park et al, 2010), medicine (Halberstadt and Douglas, 2008;Abuhamdah, 2015).…”

Clustering method based on fuzzy binary relation

“…For a more effective solution of emerging practical problems of cluster analysis, there is always the need for new clustering methods or various modifications of existing ones. Considering the needs of medicine [1], agriculture [2], economy [3] geodemography [4], various modifications of the optimization model of the fuzzy c-means algorithm are presented. The main shortcomings of c-means are the need for setting the optimum number of clusters, computing complexity, "noise" sensitivity, low rate of convergence and "sticking" at local minima.…”