9 March 2010 Segmentation and classification of dermatological lesions
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Certain skin diseases are chronic, inflammatory and without cure. However, there are many treatment options that can clear them for a period of time. Measuring their severity and assessing their extent, is a fundamental issue to determine the efficacy of the treatment under test. Two of the most important parameters of severity assessment are Erythema (redness) and Scaliness. Physicians classify these parameters into several grades by visual grading method. In this paper a color image segmentation and classification algorithm is developed to obtain an assessment of erythema and scaliness of dermatological lesions. Color digital photographs taken under an acquisition protocol form the database. Difference between green band and blue band of images in RGB color space shows two modes (healthy skin and lesion) with clear separation. Otsu's method is applied to this difference in order to isolate the lesion. After the skin disease is segmented, some color and texture features are calculated and they are the inputs to a Fuzzy-ARTMAP neural network. The neural network classifies them into the five grades of erythema and the five grades of scaliness. The method has been tested with 31 images with a success percentage of 83.87 % when the images are classified in erythema, and 77.42 % for scaliness classification.
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Aurora Sáez, Aurora Sáez, Begoña Acha, Begoña Acha, Carmen Serrano, Carmen Serrano, } "Segmentation and classification of dermatological lesions", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76243L (9 March 2010); doi: 10.1117/12.844323; https://doi.org/10.1117/12.844323


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