A two-stage approach for discriminating melanocytic skin lesions using standard cameras

Authors:
Pablo G. Cavalcanti;Jacob Scharcanski;Gladimir V. G. Baranoski
Affiliations:
Instituto de Informática, Universidade Federal do Rio Grande do Sul., Avenida Bento Gonçalves, RS 91501-970, Porto Alegre, Brazil;Instituto de Informática, Universidade Federal do Rio Grande do Sul., Avenida Bento Gonçalves, RS 91501-970, Porto Alegre, Brazil;Natural Phenomena Simulation Group, School of Computer Science, University of Waterloo, Ontario, Canada
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

Citing 10
Cited 0

Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Machine Learning (Adaptive Computation and Machine Learning)

Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Color Imaging Science

Introduction to Color Imaging Science
A multi-direction GVF snake for the segmentation of skin cancer images

Pattern Recognition
A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Light & Skin Interactions: Simulations for Computer Graphics Applications

Light & Skin Interactions: Simulations for Computer Graphics Applications
Shading attenuation in human skin color images

ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
A decision support system for the diagnosis of melanoma: A comparative approach

Expert Systems with Applications: An International Journal
Generalizing Common Tasks in Automated Skin Lesion Diagnosis

IEEE Transactions on Information Technology in Biomedicine
Automatic Skin Lesion Segmentation via Iterative Stochastic Region Merging

IEEE Transactions on Information Technology in Biomedicine

Quantified Score

Hi-index	12.05

Visualization

Abstract

In this paper, we propose a novel approach to discriminate malignant melanomas and benign atypical nevi, since both types of melanocytic skin lesions have very similar characteristics. Recent studies involving the non-invasive diagnosis of melanoma indicate that the concentrations of the two main classes of melanin present in the human skin, eumelanin and pheomelanin, can potentially be used in the computation of relevant features to differentiate these lesions. So, we describe how these features can be estimated using only standard camera images. Moreover, we demonstrate that using these features in conjunction with features based on the well known ABCD rule, it is possible to achieve 100% of sensitivity and more than 99% accuracy in melanocytic skin lesion discrimination, which is a highly desirable characteristic in a prescreening system.