Transition region based approach for skin lesion segmentation
Parida, Priyadarsan (GIET University (Rayagada, India). Department of Electronics and Communication Engineering)
Rout, Ranjita (GIET University (Rayagada, India). Department of Electronics and Communication Engineering)

Fecha:	2020
Resumen:	Skin melanoma is a skin disease that affects nearly 40% of people globally. Manual detection of the area is a time-consuming process and requires expert knowledge. The application of computer vision techniques can simplify this. In this article, a novel unsupervised transition region based approach for skin lesion segmentation for melanoma detection is proposed. The method starts with Gaussian blurring of the green channel dermoscopic image. Further, the transition region is extracted using local variance features and a global thresholding operation. It achieves the region of interest (binary mask) using various morphological operations. Finally, the melanoma regions are segregated from normal skin regions using the binary mask. The proposed method is tested using DermQuest dataset along with ISIC 2017 dataset and it achieves better results as compared to other state of art methods in effectively segmenting the melanoma regions from the normal skin regions.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Skin melanoma ; Transition region ; Morphological operation ; Gaussian blurring
Publicado en:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 19 Núm. 1 (2020) , p. 28-39 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v19-n1-Parida-Rout
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/370832
DOI: 10.5565/rev/elcvia.1177

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