A local information based variational model for selective image segmentation

Jianping Zhang; Ke Chen; Bo Yu; Derek A. Gould

doi:10.3934/ipi.2014.8.293

Article Contents

2014, Volume 8, Issue 1: 293-320. Doi: 10.3934/ipi.2014.8.293

This issue Previous Article Towards deconvolution to enhance the grid method for in-plane strain measurement Next Article PHLST with adaptive tiling and its application to antarctic remote sensing image approximation

A local information based variational model for selective image segmentation

1.
School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, 116024
2.
Centre for Mathematical Imaging Techniques and Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL
3.
School of Mathematical Science, Dalian University of Technology, Dalian, Liaoning 116024
4.
Radiology Department, Royal Liverpool University Hospitals, Prescot Street, Liverpool L7 8XP

Received: June 30, 2011

Revised: October 31, 2012

Published: January 2014

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Abstract

Many effective models are available for segmentation of an image to extract all homogenous objects within it. For applications where segmentation of a single object identifiable by geometric constraints within an image is desired, much less work has been done for this purpose. This paper presents an improved selective segmentation model, without `balloon' force, combining geometrical constraints and local image intensity information around zero level set, aiming to overcome the weakness of getting spurious solutions by Badshah and Chen's model [8]. A key step in our new strategy is an adaptive local band selection algorithm. Numerical experiments show that the new model appears to be able to detect an object possessing highly complex and nonconvex features, and to produce desirable results in terms of segmentation quality and robustness.

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Mathematics Subject Classification: Primary: 62H35, 65N22, 65N55; Secondary: 74G65.

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