In this paper, the performance of the various fuzzy based algorithms for medical image segmentation is presented. Fuzzy c-means (FCM) algorithm has proved its effectiveness for image segmentation. However, still it lacks in getting robustness to noise and outliers, especially in the absence of prior knowledge of the noise. To overcome this problem, different types of fuzzy algorithms are introd...

A generalised fuzzy approach to statistical modelling techniques for speech recognition is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to hidden Markov models (HMMs). A more robust version of the above fuzzy technique based on the noise clustering (NC) method is also proposed. Experimental results were...

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.07.112 ⇑ Corresponding author. E-mail addresses: [email protected] (H. I org (A. Abraham). Fuzzy clustering is an important problem which is the subject of active research in several real-world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient,...

This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the propo...

-Impulse noise detection is a critical issue when removing impulse noise and impulse/gaussian mixed noise. The framework combines Robust Outlyingness Ratio (ROR) detection mechanism and Fuzzy C Means (FCM) clustering algorithm and Nonlocal Means (NLM) filter. ROR for measuring how impulse like each pixel is and then all pixels are divided into four clusters according to the ROR values. The dete...

Clustering is a key process in data mining for revealing structure and patterns in data. Fuzzy C-means (FCM) is a popular algorithm using a partitioning approach for clustering. One advantage of FCM is that it converges rapidly. In addition, using fuzzy sets to represent the degrees of cluster membership of each data point provides more information regarding relationships within the data than d...

— Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. O...

Protein sequence motifs are very important to the analysis of biologically significant conserved regions to determine the conformation, function and activities of the proteins. These sequence motifs are identified from protein sequence segments generated from large number of protein sequences. All generated sequence segments may not yield potential motif patterns. In this paper, short recurring...

This paper, presents a new image segmentation method based on Wavelets, Particle Swarm Optimization (PSO) and outlier rejection caused by the membership function of the kernel fuzzy local information c-means (KFLICM) algorithm combined with level set is proposed. The segmentation of Magnetic Resonance (MR) images plays an important role in the computer-aided diagnosis and clinical research, but...

Résumé. Dans cet article, nous nous intéressons à Fuzzy C-Means (FCM), une technique très connue pour la classification floue. Nous proposons un algorithme efficace basé sur la programmation DC (Difference of Convexe functions) et DCA (DC Algorithm) pour résoudre ce problème. Les expériences numériques comparatives avec l’algorithme standard FCM sur les données réelles montrent la robustesse, l...