Massey, Elizabeth and Hunter, Andrew and Lowell, James and Steel, David (2009) Using shape entropy as a feature to lesion boundary segmentation with level sets. In: 1st International Conference on Mathematical and Computational Biomedical Engineering - CMBE2009, June 29-July 1, 2009, Swansea, Wales, UK.

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Abstract

Accurate lesion segmentation in retinal imagery is an area of vast research. Of the many segmentation methods available very few are insensitive to topological changes on noisy surfaces. This paper presents an extension to earlier work on a novel stopping mechanism for level sets. The elementary features scheme (ELS) in [5] is extended to include shape entropy as a feature used to ’look back in time’ and find the point at which the curve best fits the real object. We compare the proposed extension against the original algorithm for timing and accuracy using 50 randomly selected images of exudates with a database of clinician demarcated boundaries as ground truth. While this work is presented applied to medical imagery, it can be used for any application involving the segmentation of bright or dark blobs on noisy images.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Accurate lesion segmentation in retinal imagery is an area of vast research. Of the many segmentation methods available very few are insensitive to topological changes on noisy surfaces. This paper presents an extension to earlier work on a novel stopping mechanism for level sets. The elementary features scheme (ELS) in [5] is extended to include shape entropy as a feature used to ’look back in time’ and find the point at which the curve best fits the real object. We compare the proposed extension against the original algorithm for timing and accuracy using 50 randomly selected images of exudates with a database of clinician demarcated boundaries as ground truth. While this work is presented applied to medical imagery, it can be used for any application involving the segmentation of bright or dark blobs on noisy images.
Keywords:	Shape Special Session, Exudate Segmentation, Level Sets, Medical Image Processing.
Subjects:	G Mathematical and Computer Sciences > G400 Computer Science G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Users 501743 not found.
Date Deposited:	15 Jan 2010 10:03
Last Modified:	13 Mar 2013 08:34
URI:	http://eprints.lincoln.ac.uk/id/eprint/2132

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