Using shape entropy as a feature to lesion boundary segmentation with level sets

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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Using Shape entropy as a feature to lesion boundary segmentation with level sets
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.
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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 Science > School of Computer Science
ID Code:2132
Deposited By:INVALID USER
Deposited On:15 Jan 2010 10:03
Last Modified:13 Mar 2013 08:34

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