Fuzzy clustering methods have been used extensively for image segmentation in the past decade. The most commonly used soft clustering algorithm is Fuzzy C-Means. An improvised version of FCM called Intuitionistic Fuzzy C-Means (IFCM) has also gained popularity in the recent past. In this paper, we propose a new hybrid algorithm which combines intuitionistic fuzzy c-means and firefly algorithm to propose Intuitionistic Fuzzy C-Means with Firefly Algorithm (IFCMFA). Experimental analysis confirms that IFCMFA is far more superior to both FCM and IFCM. Several measures like DB-index and D-index are used for this purpose. Also, different types of images like MRI scan, Rice, Lena and satellite image are used as inputs to establish our claim. © 2018, Springer Nature Singapore Pte Ltd.

Balakrushna Tripathy

Department of Analytics

School of Computer Science and Engineering

Vellore Campus

Chinta S.S

Jain A

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Image Segmentation Using Hybridized Firefly Algorithm and Intuitionistic Fuzzy C-Means

Proceedings of First International Conference on Smart System, Innovations and Computing Smart Innovation, Systems and Technologies

The aim of our research is to combine the conventional clustering algorithms based on rough sets and fuzzy sets with metaheuristics like firefly algorithm and fuzzy firefly algorithm. Image segmentation is carried out using the resultant hybrid clustering algorithms. The performance of the proposed algorithms is compared with numerous contemporary clustering algorithms and their firefly fused counter-parts. We further bolster the performance of our proposed algorithm my using Gaussian kernel in place of the traditional Euclidean distance measure. We test the performance of our algorithms using two performance indices, namely DB (Davis Bouldin) indexand Dunn index. Our experimental results highlight the advantages of using metaheuristics and kernels over the existing clustering algorithms. © 2018, Springer Nature Singapore Pte Ltd.

Soft Computing Systems Communications in Computer and Information Science

Kernelised Clustering Algorithms Fused with Firefly and Fuzzy Firefly Algorithms for Image Segmentation

From the beginning of the data analysis system cluster computing plays an important role on it. The very early developed clustering algorithms which can handle only numerical data and K-means clustering is one of them and was proposed by Macqueen [1] in 1967. This algorithm helps us to find the homogeneity of the data set. This K-means algorithm has been modified in many ways to get the modified K-means and kernel based K-means is one of them. It is a nonlinear transformation which transforms the sample data into high dimensional feature space. Though this kernel based K-means performs good almost on every data set but it is unable to handle uncertainty. After rough set theory has been proposed by Pawlak [2], we have many clustering algorithms based on it which can handle uncertainty and heterogeneous data and Rough based K-means is one of them. So in this paper we are proposing the combination of these two methods and known as kernel based K-Means using rough set. © 2012 IEEE.

2012 International Conference on Computer Communication and Informatics

Kernel based K-means clustering using rough set

Soft Computing

Intuitionistic fuzzy c -means clustering algorithm with neighborhood attraction in segmenting medical image

American Journal of Applied Sciences

A HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATION

Applied Soft Computing

A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images

International Journal of Bio-Inspired Computation

Firefly algorithm, stochastic test functions and design optimisation

Journal	Data powered by TypesetProceedings of First International Conference on Smart System, Innovations and Computing Smart Innovation, Systems and Technologies
Publisher	Data powered by TypesetSpringer Singapore
ISSN	2190-3018
Open Access	0