NDLI: Segmentation through a local and adaptive weighting scheme, for contour-based blending of image and prior information

Content Provider	IEEE Xplore Digital Library
Author	Zarpalas, D. Gkontra, P. Daras, P. Maglaveras, N.
Copyright Year	2012
Description	Author affiliation: Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece (Zarpalas, D.; Gkontra, P.; Daras, P.) \|\| Medical Informatics Laboratory, Aristotle University of Thessaloniki, Greece (Maglaveras, N.)
Abstract	Active Contour Models have been widely used in computer vision for segmentation purposes, while anatomically constrained ACMs have offered a valuable solution on medical image segmentation. Efforts have been devoted on various ways of modeling prior knowledge. This paper focuses on how to efficiently incorporate prior knowledge, into an ACM evolution framework, using the structures' distribution map as a second feature image, and blending the two images through a novel adaptive local weighting scheme. For proof of concept the method is applied on hippocampus segmentation in T1-MR brain images, a very challenging task, due to its multivariate surrounding region and the weak, even missing boundaries.
Starting Page	1
Ending Page	4
File Size	193927
Page Count	4
File Format	PDF
ISBN	9781467320498
ISSN	10637125
e-ISBN	9781467320511
DOI	10.1109/CBMS.2012.6266319
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-20
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Image segmentation Hippocampus Training Biomedical imaging Image edge detection Brain modeling
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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