Segmentation in noisy medical images using PCA model based particle filtering
Wei Qu, Xiaolei Huang, Yuanyuan Jia · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
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.