Paper
19 June 2015 Asymmetry and irregularity border as discrimination factor between melanocytic lesions
David Sbrissa, Sebastião Pratavieira, Ana Gabriela Salvio, Cristina Kurachi, Vanderlei Salvadori Bagnato, Luciano Da Fontoura Costa, Gonzalo Travieso
Author Affiliations +
Proceedings Volume 9531, Biophotonics South America; 953122 (2015) https://doi.org/10.1117/12.2186180
Event: SPIE Biophotonics South America, 2015, Rio de Janeiro, Brazil
Abstract
Image processing tools have been widely used in systems supporting medical diagnosis. The use of mobile devices for the diagnosis of melanoma can assist doctors and improve their diagnosis of a melanocytic lesion. This study proposes a method of image analysis for melanoma discrimination from other types of melanocytic lesions, such as regular and atypical nevi. The process is based on extracting features related with asymmetry and border irregularity. It were collected 104 images, from medical database of two years. The images were obtained with standard digital cameras without lighting and scale control. Metrics relating to the characteristics of shape, asymmetry and curvature of the contour were extracted from segmented images. Linear Discriminant Analysis was performed for dimensionality reduction and data visualization. Segmentation results showed good efficiency in the process, with approximately 88:5% accuracy. Validation results presents sensibility and specificity 85% and 70% for melanoma detection, respectively.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David Sbrissa, Sebastião Pratavieira, Ana Gabriela Salvio, Cristina Kurachi, Vanderlei Salvadori Bagnato, Luciano Da Fontoura Costa, and Gonzalo Travieso "Asymmetry and irregularity border as discrimination factor between melanocytic lesions", Proc. SPIE 9531, Biophotonics South America, 953122 (19 June 2015); https://doi.org/10.1117/12.2186180
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KEYWORDS
Image segmentation

Melanoma

Image processing

Image analysis

Medical imaging

Databases

Cancer

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