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Published in Rahimi, S., Zargham, M., Thakre, A., & Chhillar, D. (2004). A parallel Fuzzy C-Mean algorithm for image segmentation. IEEE Annual Meeting of the Fuzzy Information Processing Society, 2004. NAFIPS '04, 234-237. doi; 10.1109/NAFIPS.2004.1336283 ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

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. such as medical image segmentation and geographical image analysis that deal with large size images, sequenrial 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

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