Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/57477
Title: Segmentation and Classification of Skin Lesions from Dermoscopic Images
Authors: Palivela, Lakshmi Harika
Athanesious, Joshan J
Deepika, V
Vignesh, M
Keywords: Deep learning;Feature extraction;Image processing;Melanoma-cancer;Texture
Issue Date: Apr-2021
Publisher: NISCAIR-CSIR, India
Abstract: Skin melanoma cancer, particularly among non-Hispanic white women and men, has been one of the highest risks of spreading disease among all cancers. It should be treated earlier for effective treatment. Due to high costs of screening each patient by dermatologists, it is important to establish an automated method to determine the risk of melanoma for a patient by using image scan of their skin lesions that can provide accurate diagnosis. The major challenge is segmenting the skin lesion from the digital scan image. For segmenting the lesion, a novel algorithm based on skin texture is proposed in which a set of representative texture distributions is analysed from a non-illuminated image. The ridge in the skin image is labeled as either normal segment or lesion, based on the presence of sample texture distributions by calculating the texture distinctiveness metrics. In comparison with other bench-mark models the suggested algorithm has greater precision in segmentation about 95% accuracy.
Page(s): 328-335
URI: http://nopr.niscair.res.in/handle/123456789/57477
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.80(04) [April 2021]

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