Segmentation in noisy medical images using PCA model based particle filtering

Wei Qu, Xiaolei Huang, Yuanyuan Jia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2008
Subtitle of host publicationImage Processing
StatePublished - 2008
EventMedical Imaging 2008: Image Processing - San Diego, CA, United States
Duration: Feb 17 2008Feb 19 2008

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2008: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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