NDLI: A new multi-level framework for deformable contour optimization

Content Provider	IEEE Xplore Digital Library
Author	Akgul, Y.S. Kambhamettu, C.
Copyright Year	1999
Description	Author affiliation: Dept. of Comput. & Inf. Sci., Delaware Univ., Newark, DE, USA (Akgul, Y.S.)
Abstract	Application of dynamic programming to the deformable contours has many advantages, such as guaranteed optimality and numerical stability. However, long execution times of these methods almost always force researchers to use dynamic programming in combination with multiresolution methods. Multiresolution methods shorten the execution time by subsampling the original images after an application of a smoothing filter. However, this speedup comes at the expense of contour optimality due to the loss of details in the decreased resolution. In this paper, we present a new multi-level framework for deformable contour optimization, which can achieve faster optimization times and performs better than current multiresolution methods. To form the new levels, this method uses a very efficient algorithm to segment the original images with respect to the deformable contour external energy instead of subsampling. An exhaustive search on these segments is carried out by dynamic programming. A novel gradient descent algorithm is employed to find optimal internal energy for large image segments, where the external energy remains constant due to segmentation. We also introduce a new algorithm to pass the contour information more precisely between the levels. We present an analysis of time and performance comparisons with the current multiresolution methods by the experiments done on variety of medical images, which confirmed efficiency and accuracy of our framework.
Starting Page	465
Ending Page	470
File Size	889119
Page Count	6
File Format	PDF
ISBN	0769501494
ISSN	10636919
DOI	10.1109/CVPR.1999.784722
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy resolution Image segmentation Dynamic programming Image resolution Optimization methods Numerical stability Smoothing methods Filters Image analysis Performance analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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