Semi-automatic segmentation of skin lesions based on superpixels and hybrid texture information

•A semi-automatic segmentation method for dermoscopic image is proposed.•Related state-of-art is reviewed.•Five public image datasets are used in the experiments;•The results obtained by the proposed methods are very promising.•The results confirm the high robustness of the proposed method. [Display...

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Bibliographic Details
Published in:	Medical image analysis Vol. 77; p. 102363
Main Authors:	Santos, Elineide S. dos, de M S Veras, Rodrigo, R T Aires, Kelson, M B F Portela, Helano, Braz Junior, Geraldo, Santos, Justino D., Tavares, João Manuel R.S.
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01-04-2022 Elsevier BV
Subjects:	Algorithms Clustering Computer applications Datasets Dermis Image processing Image segmentation Iterative methods Lesions Parameters Skin diseases Skin lesions Texture Dermatoscopic image segmentation Texture information 41A10 Melanoma 65D05 65D17 41A05 Computer-aided diagnosis
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Summary:	•A semi-automatic segmentation method for dermoscopic image is proposed.•Related state-of-art is reviewed.•Five public image datasets are used in the experiments;•The results obtained by the proposed methods are very promising.•The results confirm the high robustness of the proposed method. [Display omitted] Dermoscopic images are commonly used in the early diagnosis of skin lesions, and several computational systems have been proposed to analyze them. The segmentation of the lesions is a fundamental step in many of these systems. Therefore, a semi-automatic segmentation method is proposed here, which begins by building the superpixels of the image under analysis based on the zero parameter version of the simple linear iterative clustering (SLIC0) algorithm. Then, each superpixel is represented using a descriptor built by combining the grey-level co-occurrence matrix and Tamura texture features. Afterward, the gain ratios of the features are used to select the input for the semi-supervised seeded fuzzy C-means clustering algorithm. Hence, from a few specialist-selected superpixels, this clustering algorithm groups the built superpixels into lesion or background regions. Finally, the segmented image undergoes a post-processing step to eliminate sharp edges. The experiments were performed on 1380 images: 401 images from the PH2 and DermIS datasets, which were used to establish the parameters of the method, and 3,573 images from the ISIC 2016, ISIC 2017 and ISIC 2018 datasets were used for the analysis of the method’s performance. The findings suggest that, by manually identifying just a few of the generated superpixels, the method can achieve an average segmentation accuracy of 96.78%, which confirms its superiority to the ones in the literature.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1361-8415 1361-8423
DOI:	10.1016/j.media.2022.102363