Optimized kernel fuzzy c means (OKFCM) clustering algorithm on level set method for noisy images

Author

Saikumar, Tara ; Preetam, I. Neenu

Author_Institution

Dept. of ECE, JNTUH, Hyderabad, India

fYear

2013

fDate

26-28 Dec. 2013

Firstpage

1

Lastpage

5

Abstract

In this paper, optimized kernel fuzzy c-means (OKFCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. Firstly, OKFCM algorithm computes the fuzzy membership values for each pixel. On the basis of OKFCM the edge indicator function was redefined. Using the edge indicator function the segmentation of medical images which are added with salt and pepper noise was performed to extract the regions of interest for further processing. The results of the above process of segmentation showed a considerable improvement in the evolution of the level set function.

Keywords

feature extraction; image denoising; image segmentation; medical image processing; pattern clustering; set theory; OKFCM clustering algorithm; curve propagation; edge indicator function; fuzzy membership values; initial contour curve; level set method; medical image segmentation; noisy image; optimized kernel fuzzy c-means clustering algorithm; regions-of-interest extraction; salt-and-pepper noise; Biomedical imaging; Clustering algorithms; Image edge detection; Image segmentation; Kernel; Level set; Pattern recognition; Image segmentation; OKFCM; images; level set method;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on

Conference_Location

Enathi

Print_ISBN

978-1-4799-1594-1

Type

conf

DOI

10.1109/ICCIC.2013.6724290

Filename

6724290