Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms

• Published in: Computers in biology and medicine Vol. 41; no. 9; pp. 742 - 755
• Main Authors: Gola Isasi, A., García Zapirain, B., Méndez Zorrilla, A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2011; Elsevier Limited
• Subjects: Algorithms; Automated diagnosis; color; Databases, Factual; Humans; image analysis; Image Interpretation, Computer-Assisted - methods; Image processing; Internal Medicine; melanoma; Melanoma - diagnosis; Melanoma - pathology; Melanomas; Mortality; Other; Pattern recognition; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Skin cancer; Skin Neoplasms - diagnosis; Skin Neoplasms - pathology; Software; Studies; Spain; Pattern recognition; Image processing; Melanomas; Automated diagnosis
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2011.06.010

In this paper an automated dermatological tool for the parameterization of melanomas is presented. The system is based on the standard ABCD Rule and dermatological Pattern Recognition protocols. On the one hand, a complete stack of algorithms for the asymmetry, border, color, and diameter parameterization were developed. On the other hand, three automatic algorithms for digital image processing have been developed in order to detect the appropriate patterns. These allow one to calculate certain quantitative features based on the aspect and inner patterns of the melanoma using simple-operation algorithms, in order to minimize response time. The database used consists of 160 500×500-pixel RGB images (20 images per pattern) cataloged by dermatologists, and the results have turned out to be successful according to assessment by medical experts. While the ABCD algorithms are mathematically reliable, the proposed algorithms for pattern recognition produced a remarkable rate of globular, reticular, and blue veil Pattern recognition, with an average above 85% of accuracy. It thus proves to be a reliable system when performing a diagnosis.