11 March 2008 Segmentation in noisy medical images using PCA model based particle filtering
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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.
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Wei Qu, Wei Qu, Xiaolei Huang, Xiaolei Huang, Yuanyuan Jia, 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); doi: 10.1117/12.769852; https://doi.org/10.1117/12.769852
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