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Robust Vessel Segmentation Based on Multi-resolution Fuzzy Clustering

  • Gang Yu
  • Pan Lin
  • Shengzhen Cai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

A novel multi-resolution approach is presented for vessel segmentation using multi-scale fuzzy clustering and vessel enhancement filtering. According to geometric shape analysis of the vessel structure with different scale, a new fuzzy inter-scale constraint based on antistrophic diffusion linkage model is introduced which builds an efficient linkage relationship between the high resolution feature images and low resolution ones. Meanwhile, this paper develops two new fuzzy distances which describe the fuzzy similarity of line-like structure in adjacent scales effectively. Moreover, a new multiresolution framework combining the inter- and intra-scale constraints is presented. The proposed framework is robust to noisy vessel images and low contrast ones, such as medical images. Segmentation of a number of vessel images shows that the proposed approach is robust and accurate.

Keywords

Segmentation Result Fuzzy Cluster Vessel Segmentation Vessel Image Fuzzy Similarity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gang Yu
    • 1
  • Pan Lin
    • 2
  • Shengzhen Cai
    • 2
  1. 1.School of Info-Physics and Geometics EngineeringCentral South UniversityHunanChina
  2. 2.Faculty of SoftwareFujian Normal UniversityFujianChina

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