Adaptive Kalman snake for semi-autonomous 3D vessel tracking

Sang Hoon Lee, Sanghoon Lee

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

In this paper, we propose a robust semi-autonomous algorithm for 3D vessel segmentation and tracking based on an active contour model and a Kalman filter. For each computed tomography angiography (CTA) slice, we use the active contour model to segment the vessel boundary and the Kalman filter to track position and shape variations of the vessel boundary between slices. For successful segmentation via active contour, we select an adequate number of initial points from the contour of the first slice. The points are set manually by user input for the first slice. For the remaining slices, the initial contour position is estimated autonomously based on segmentation results of the previous slice. To obtain refined segmentation results, an adaptive control spacing algorithm is introduced into the active contour model. Moreover, a block search-based initial contour estimation procedure is proposed to ensure that the initial contour of each slice can be near the vessel boundary. Experiments were performed on synthetic and real chest CTA images. Compared with the well-known Chan-Vese (CV) model, the proposed algorithm exhibited better performance in segmentation and tracking. In particular, receiver operating characteristic analysis on the synthetic and real CTA images demonstrated the time efficiency and tracking robustness of the proposed model. In terms of computational time redundancy, processing time can be effectively reduced by approximately 20%.

Original languageEnglish
Pages (from-to)56-75
Number of pages20
JournalComputer Methods and Programs in Biomedicine
Volume122
Issue number1
DOIs
Publication statusPublished - 2015 Oct 1

Fingerprint

Snakes
Angiography
Tomography
Kalman filters
ROC Curve
Thorax
Redundancy
Computed Tomography Angiography
Processing
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Health Informatics

Cite this

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Adaptive Kalman snake for semi-autonomous 3D vessel tracking. / Lee, Sang Hoon; Lee, Sanghoon.

In: Computer Methods and Programs in Biomedicine, Vol. 122, No. 1, 01.10.2015, p. 56-75.

Research output: Contribution to journalArticle

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