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A Robust MR Image Segmentation Technique Using Spatial Information and Principle Component Analysis

  • Yen-Wei Chen
  • Yuuta Iwasaki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Automated segmentation of MR images is a difficult problem due to the complexity of the images. Up to now, several approaches have been proposed based on spectral characteristics of MR images, but they are sensitive to the noise contained in the MR images. In this paper, we propose a robust method for noisy MR image segmentation. We use region-based features for a robust segmentation and use principle component analysis (PCA) to reduce the large dimensionality of feature space. Experimental results show that the proposed method is very tolerant to the noise and the segmentation performance is significantly improved.

Keywords

False Negative Rate Principle Component Analysis Target Class Segmentation Performance Proton Density Image 
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 2006

Authors and Affiliations

  • Yen-Wei Chen
    • 1
    • 2
  • Yuuta Iwasaki
    • 2
  1. 1.College of Electronic and Information EngineeringCentral South Forest UniversityChangshaChina
  2. 2.College of Information Science and EngineeringRitsumeikan UniversityKusatsu, ShigaJapan

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