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Authors: Kaushik V. S. N. Ghantasala 1 ; Raeed H. Chowdhury 2 ; Uday Guntupalli 1 ; Jason Hagerty 3 ; Randy H. Moss 1 ; Ryan K. Rader 2 and William V. Stoecker 2

Affiliations: 1 Missouri University of Science And Technology and G20 Emerson Electrical Co. Hall, United States ; 2 Stoecker & Associates, United States ; 3 Missouri University of Science And Technology, G20 Emerson Electrical Co. Hall and Stoecker & Associates, United States

ISBN: 978-989-8565-47-1

ISSN: 2184-4321

Keyword(s): Median Split, Melanoma, Image Analysis, Color Processing, Dermoscopy.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Medical Image Applications ; Segmentation and Grouping

Abstract: Detection of melanoma remains an empirical clinical science. New tools for automatic discrimination of melanoma from benign lesions in digitized dermoscopy images may allow an improvement in early detection of melanoma. This research implements a fast version of the median split algorithm in an open source format and applied to four-color splitting of the lesion area to capture the architectural disorder apparent in melanoma colors. Our version of the median split algorithm splits colors along the color axis with maximum Range. For a set of 888 dermoscopy images, the best model for discrimination produces an area under the receiver operating characteristic curve of 0.821. Logistic regression analysis of 242 parameter variables obtained from 888 images shows that the most important features in the final model, measured by Wald Chi-square significance, are the lengths of two peripheral inter-color boundaries and one measure of boundary overlay by different colors. The median split alg orithm is fast, requiring less than one second per image and only a four-color splitting, but it captures sufficient critical information regarding color disorder, with peripheral inter-color boundaries showing the highest significance for melanoma discrimination. (More)

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Paper citation in several formats:
Ghantasala, K.; Chowdhury, R.; Guntupalli, U.; Hagerty, J.; Moss, R.; Rader, R. and Stoecker, W. (2013). The Median Split Algorithm for Detection of Critical Melanoma Color Features.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, ISSN 2184-4321, pages 492-495. DOI: 10.5220/0004304904920495

@conference{visapp13,
author={Kaushik V. S. N. Ghantasala. and Raeed H. Chowdhury. and Uday Guntupalli. and Jason Hagerty. and Randy H. Moss. and Ryan K. Rader. and William V. Stoecker.},
title={The Median Split Algorithm for Detection of Critical Melanoma Color Features},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={492-495},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004304904920495},
isbn={978-989-8565-47-1},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - The Median Split Algorithm for Detection of Critical Melanoma Color Features
SN - 978-989-8565-47-1
AU - Ghantasala, K.
AU - Chowdhury, R.
AU - Guntupalli, U.
AU - Hagerty, J.
AU - Moss, R.
AU - Rader, R.
AU - Stoecker, W.
PY - 2013
SP - 492
EP - 495
DO - 10.5220/0004304904920495

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