Segmentation in noisy medical images using PCA model based particle filtering

Wei Qu; Xiaolei Huang; Yuanyuan Jia

doi:10.1117/12.769852

11 March 2008 Segmentation in noisy medical images using PCA model based particle filtering

Wei Qu, Xiaolei Huang, Yuanyuan Jia

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 69143I (2008) https://doi.org/10.1117/12.769852
Event: Medical Imaging, 2008, San Diego, California, United States

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.

Citation Download Citation

Wei Qu, Xiaolei Huang, and Yuanyuan Jia "Segmentation in noisy medical images using PCA model based particle filtering", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69143I (11 March 2008); https://doi.org/10.1117/12.769852

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