TAILIEUCHUNG - Báo cáo hóa học: " Research Article Fuzzy Image Segmentation Using Membership Connectedness"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Fuzzy Image Segmentation Using Membership Connectedness | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 417293 13 pages doi 2008 417293 Research Article Fuzzy Image Segmentation Using Membership Connectedness Maryam Hasanzadeh and Shohreh Kasaei Computer Engineering Department Sharif University of Technology Tehran 11155-9517 Iran Correspondence should be addressed to Shohreh Kasaei skasaei@ Received 27 July 2008 Revised 11 September 2008 Accepted 15 October 2008 Recommended by Stephen Marshall Fuzzy connectedness and fuzzy clustering are two well-known techniques for fuzzy image segmentation. The former considers the relation of pixels in the spatial space but does not inherently utilize their feature information. On the other hand the latter does not consider the spatial relations among pixels. In this paper a new segmentation algorithm is proposed in which these methods are combined via a notion called membership connectedness. In this algorithm two kinds of local spatial attractions are considered in the functional form of membership connectedness and the required seeds can be selected automatically. The performance of the proposed method is evaluated using a developed synthetic image dataset and both simulated and real brain magnetic resonance image MRI datasets. The evaluation demonstrates the strength of the proposed algorithm in segmentation of noisy images which plays an important role especially in medical image applications. Copyright 2008 M. Hasanzadeh and S. Kasaei. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Image segmentation is one of the most challenging and critical problems in image analysis. Segmentation processes aim at partitioning the image plane into meaningful regions where meaningful typically refers to separation of image regions into .

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