Melanoma detection on dermoscopic images using  superpixels segmentation and shape-based features

Diego Patiño; Alberto M. Ceballos-Arroyo; Jairo A. Rodriguez-Rodriguez; German Sanchez-Torres; John W. Branch-Bedoya

doi:10.1117/12.2545300

3 January 2020 Melanoma detection on dermoscopic images using superpixels segmentation and shape-based features

Diego Patiño, Alberto M. Ceballos-Arroyo, Jairo A. Rodriguez-Rodriguez, German Sanchez-Torres, John W. Branch-Bedoya

Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 1133018 (2020) https://doi.org/10.1117/12.2545300
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia

Abstract

In this work, we present a shape-based approach to automatic skin lesion segmentation and classification in dermoscopic images. We aim to differentiate three types of lesion 1) common nevi, 2) atypical nevi, and 3) melanomas by exploring the morphology features of segmented skin lesions. Our method is an attempt to design a computer-aided ABCDEs of melanoma, where the Asymmetry and Border components are estimated using morphological features. The lesions are first segmented using a super-pixel merging strategy with an RGB criterion. Later, the segmentation method was evaluated on the PH² dataset, and compared with other state-of- the-art skin segmentation methods. The classification was also conducted on the PH² dataset through a 10-fold cross-validation set-up with a training and testing set partition of 90% and 10% respectively. We employed logistic regression, SVM and a neural network as classification algorithms. The best performances was 86.5% on average with the neural network.

Citation Download Citation

Diego Patiño, Alberto M. Ceballos-Arroyo, Jairo A. Rodriguez-Rodriguez, German Sanchez-Torres, and John W. Branch-Bedoya "Melanoma detection on dermoscopic images using superpixels segmentation and shape-based features", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 1133018 (3 January 2020); https://doi.org/10.1117/12.2545300

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