Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

Novel active contour model based on multi-variate local Gaussian distribution for local... Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images’ information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images’ information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Optical Review Springer Journals

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

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Publisher
Springer Journals
Copyright
Copyright © 2017 by The Optical Society of Japan
Subject
Physics; Optics, Lasers, Photonics, Optical Devices; Atomic, Molecular, Optical and Plasma Physics; Quantum Optics; Microwaves, RF and Optical Engineering
ISSN
1340-6000
eISSN
1349-9432
D.O.I.
10.1007/s10043-017-0362-7
Publisher site
See Article on Publisher Site

Abstract

Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images’ information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images’ information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

Journal

Optical ReviewSpringer Journals

Published: Sep 6, 2017

References

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