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Integrating Feature Direction Information with a Level Set Formulation for Image Segmentation

Part of: Optics, electromagnetic theory - General

Published online by Cambridge University Press:  27 January 2016

Meng Li  and
Yi Zhan
Meng Li*
Affiliation:
Department of Mathematics, Key Laboratory of Group & Graph Theories and Applications, Chongqing University of Arts and Sciences, Yongchuan Chongqing 402160, China
Yi Zhan
Affiliation:
College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China
*
*Corresponding author. Email address:[email protected] (M. Li)
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Abstract

A feature-dependent variational level set formulation is proposed for image segmentation. Two second order directional derivatives act as the external constraint in the level set evolution, with the directional derivative across the image features direction playing a key role in contour extraction and another only slightly contributes. To overcome the local gradient limit, we integrate the information from the maximal (in magnitude) second-order directional derivative into a common variational framework. It naturally encourages the level set function to deform (up or down) in opposite directions on either side of the image edges, and thus automatically generates object contours. An additional benefit of this proposed model is that it does not require manual initial contours, and our method can capture weak objects in noisy or intensity-inhomogeneous images. Experiments on infrared and medical images demonstrate its advantages.

Keywords

Image segmentationlevel set evolutionvariational modelimage featuremaximal second-order directional derivative

MSC classification

Secondary: 78A48: Composite media; random media
Type
Research Article
Information
East Asian Journal on Applied Mathematics , Volume 6 , Issue 1 , February 2016 , pp. 1 - 22
Copyright
Copyright © Global-Science Press 2016 

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