The Median Split Algorithm for Detection of Critical Melanoma Color Features
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 algorithm 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.
V. S. Ghantasala et al., "The Median Split Algorithm for Detection of Critical Melanoma Color Features," Proceedings of the 8th International Conference on Computer Vision Theory and Applications (2013, Barcelona, Spain), vol. 1, pp. 492 - 495, SciTePress, Feb 2013.
The definitive version is available at https://doi.org/10.5220/0004304904920495
8th International Conference on Computer Vision Theory and Applications (2013: Feb. 21-24, Barcelona, Spain)
Electrical and Computer Engineering
Keywords and Phrases
Color Processing; Critical Information; Dermoscopy; Logistic Regression Analysis; Median Split; Melanoma; Parameter Variable; Receiver Operating Characteristic Curves; Color; Dermatology; Diagnosis; Image Analysis; Oncology; Regression Analysis; Algorithms
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Feb 2013