Automatic Image Characterization of Psoriasis Lesions

Javier Martínez-Torres, Alicia Silva Piñeiro, Álvaro Alesanco, Ignacio Pérez-Rey and José García
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Javier Martínez-Torres: Department of Applied Mathematics I, Telecommunications Engineering School, University of Vigo, 36310 Vigo, Spain
Alicia Silva Piñeiro: Department of Applied Mathematics I, Telecommunications Engineering School, University of Vigo, 36310 Vigo, Spain
Álvaro Alesanco: Department of Electronics Engineering and Communications, Aragón Institute of Engineering Research (I3A), University of Zaragoza, 50018 Zaragoza, Spain
Ignacio Pérez-Rey: Área de Geotecnia Básica y Experimental, Laboratorio de Geotecnia, CEDEX, 28014 Madrid, Spain
José García: Department of Electronics Engineering and Communications, Aragón Institute of Engineering Research (I3A), University of Zaragoza, 50018 Zaragoza, Spain

Mathematics, 2021, vol. 9, issue 22, 1-13

Abstract: Psoriasis is a chronic skin disease that affects 125 million people worldwide and, particularly, 2% of the Spanish population, characterized by the appearance of skin lesions due to a growth of the epidermis that is seven times larger than usual. Its diagnosis and monitoring are based on the use of methodologies for measuring the severity and extent of these spots, and this includes a large subjective component. For this reason, this paper presents an automatic method for characterizing psoriasis images that is divided into four parts: image preparation or pre-processing, feature extraction, classification of the lesions, and the obtaining of parameters. The methodology proposed in this work covers different digital-image processing techniques, namely, marker-based image delimitation, hair removal, nipple detection, lesion contour detection, areal-measurement-based lesion classification, as well as lesion characterization by means of red and white intensity. The results obtained were also endorsed by a professional dermatologist. This methodology provides professionals with a common software tool for monitoring the different existing typologies, which proved satisfactory in the cases analyzed for a set of 20 images corresponding to different types of lesions.

Keywords: psoriasis; image processing; OpenCV; classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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