A parallel Fuzzy C-Mean algorithm for image segmentation

Author

Rahimi, S. ; Zargham, M. ; Thakre, A. ; Chhillar, D.

Author_Institution

Dept. of Comput. Sci., Southern Illinois Univ., Carbondale, IL, USA

Volume

1

fYear

2004

fDate

27-30 June 2004

Firstpage

234

Abstract

This paper proposes a parallel Fuzzy C-Mean (FCM) algorithm for image segmentation. The sequential FCM algorithm is computationally intensive and has significant memory requirements. For many applications such as medical image segmentation and geographical image analysis that deal with large size images, sequential FCM is very slow. In our parallel FCM algorithm, dividing the computations among the processors and minimizing the need for accessing secondary storage, enhance the performance and efficiency of image segmentation task as compared to the sequential algorithm.

Keywords

fuzzy set theory; image colour analysis; image recognition; image segmentation; minimisation; parallel algorithms; pattern clustering; image colour analysis; image recognition; image segmentation; minimisation; parallel fuzzy c-mean algorithm; pattern clustering; secondary storage accessment; sequential fuzzy c-mean algorithm; Application software; Clustering algorithms; Computer science; Fuzzy sets; Image processing; Image recognition; Image segmentation; Image storage; Partitioning algorithms; Prototypes;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information, 2004. Processing NAFIPS '04. IEEE Annual Meeting of the

Print_ISBN

0-7803-8376-1

Type

conf

DOI

10.1109/NAFIPS.2004.1336283

Filename

1336283