Abstract
Chickenpox is a viral disease characterized by itchy skin vesicles that can have severe complications in adults. A tool for automatic detection of these lesions in patients' photographs is highly desirable to help the physician in the diagnosis. In this work we design a method for detection of chickenpox skin lesions in images. It is a combination of image processing techniques - color transform, equalization, edge detection, circular Hough transform- and statistical tests. We obtain highly satisfactory results in the detection of chickenpox vesicles, the elimination of false detections using the Kullback Leibler divergence, and in preliminary tests for discrimination between chickenpox and herpes zoster. © 2012 Springer-Verlag.
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CITATION STYLE
Oyola, J., Arroyo, V., Ruedin, A., & Acevedo, D. (2012). Detection of chickenpox vesicles in digital images of skin lesions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7441 LNCS, pp. 583–590). https://doi.org/10.1007/978-3-642-33275-3_72
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