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Proceedings Paper

Segmentation in noisy medical images using PCA model based particle filtering
Author(s): Wei Qu; Xiaolei Huang; Yuanyuan Jia
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Paper Abstract

Existing common medical image segmentation algorithms such as snake or graph cut usually could not generate satisfying results for noisy medical images such as X-ray angiographical and ultrasound images where the image quality is very poor including substantial background noise, low contrast, clutter, etc. In this paper, we present a robust segmentation method for noisy medical image analysis using Principle Component Analysis (PCA) based particle filtering. It exploits the prior clinical knowledge of desired object's shape through a PCA model. The preliminary results have shown the effectiveness and efficiency of the proposed approach on both synthetic and real clinical data.

Paper Details

Date Published: 11 March 2008
PDF: 7 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69143I (11 March 2008); doi: 10.1117/12.769852
Show Author Affiliations
Wei Qu, Siemens Medical Solutions USA Inc. (United States)
Xiaolei Huang, Lehigh Univ. (United States)
Yuanyuan Jia, Univ. of Illinois at Chicago (United States)


Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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