Homogeneity- and density distance-driven active contours for medical image segmentation

Abstract In this paper, we present a novel active contour (AC) model for medical image segmentation that is based on a convex combination of two energy functionals to both minimize the inhomogeneity within an object and maximize the distance between the object and the background. This combination is...

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Bibliographic Details
Published in:Computers in biology and medicine 2011-05, Vol.41 (5), p.292-301
Main Authors: Truc, Phan Tran Ho, Kim, Tae-Seong, Lee, Sungyoung, Lee, Young-Koo
Format: Article
Language:English
Subjects:
Active contours
Algorithms
Bone and Bones - pathology
Diagnostic Imaging - methods
Humans
Image Processing, Computer-Assisted - methods
Image segmentation
Internal Medicine
Level set methods
Magnetic Resonance Imaging - methods
Medical images
Medical Informatics - methods
Models, Statistical
Other
Pattern Recognition, Automated - methods
Phantoms, Imaging
Reproducibility of Results
Sensitivity and Specificity
Tomography, X-Ray Computed - methods
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Summary:Abstract In this paper, we present a novel active contour (AC) model for medical image segmentation that is based on a convex combination of two energy functionals to both minimize the inhomogeneity within an object and maximize the distance between the object and the background. This combination is necessary because objects in medical images, e.g., bones, are usually highly inhomogeneous while distinct organs should generate distinct image configurations. Compared with the conventional Chan–Vese AC, the proposed model yields similar performance in a set of CT images but performs better in an MRI data set, which is generally in lower contrast.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2011.03.006