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Signal, Image and Video Processing

, Volume 5, Issue 1, pp 39–47 | Cite as

A robust method based on ICA and mixture sparsity for edge detection in medical images

  • Xian-Hua HanEmail author
  • Yen-Wei Chen
Original Paper

Abstract

In this paper, a robust edge detection method based on independent component analysis (ICA) was proposed. It is known that most of the ICA basis functions extracted from images are sparse and similar to localized and oriented receptive fields. In this paper, the L p norm is used to estimate sparseness of the ICA basis functions, and then, the sparser basis functions were selected for representing the edge information of an image. In the proposed method, a test image is first transformed by ICA basis functions, and then, the high-frequency information can be extracted with the components of the selected sparse basis functions. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the noise-free image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method for edge detection is demonstrated by experiments with some medical images.

Keywords

Independent component analysis Edge detection Medical images 

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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.College of Information Science and EngineeringRitsumeikan UniversityKasatsu-shiJapan

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