Enhanced particle-filtering framework for vessel segmentation and tracking

Sang Hoon Lee, Jiwoo Kang, Sanghoon Lee

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Background and Objectives A robust vessel segmentation and tracking method based on a particle-filtering framework is proposed to cope with increasing demand for a method that can detect and track vessel anomalies. Methods We apply the level set method to segment the vessel boundary and a particle filter to track the position and shape variations in the vessel boundary between two adjacent slices. To enhance the segmentation and tracking performances, the importance density of the particle filter is localized by estimating the translation of an object's boundary. In addition, to minimize problems related to degeneracy and sample impoverishment in the particle filter, a newly proposed weighting policy is investigated. Results Compared to conventional methods, the proposed algorithm demonstrates better segmentation and tracking performances. Moreover, the stringent weighting policy we proposed demonstrates a tendency of suppressing degeneracy and sample impoverishment, and higher tracking accuracy can be obtained. Conclusions The proposed method is expected to be applied to highly valuable applications for more accurate three-dimensional vessel tracking and rendering.

Original languageEnglish
Pages (from-to)99-112
Number of pages14
JournalComputer Methods and Programs in Biomedicine
Publication statusPublished - 2017 Sept

Bibliographical note

Publisher Copyright:
© 2017 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Software
  • Health Informatics
  • Computer Science Applications


Dive into the research topics of 'Enhanced particle-filtering framework for vessel segmentation and tracking'. Together they form a unique fingerprint.

Cite this