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Fuzzy Description of Skin Lesion

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Authors: Nikolaos Lascaris ; Lucia Ballerini ; Robert Fisher ; Ben Aldridge ; Jonathan Rees

We propose a system for describing skin lesions images based on a human perception model. Pigmented skin lesions including melanoma and other types of skin cancer as well as non-malignant lesions are used. Works on classification of skin lesions already exist but they mainly concentrate on melanoma. The novelty of our work is that our system gives to skin lesion images a semantic label in a manner similar to humans. This work consists of two parts: first we capture they way users perceive each lesion, second we train a machine learning system that simulates how people describe images. For the first part, we choose 5 attributes: colour (light to dark), colour uniformity (uniform to non-uniform), symmetry (symmetric to non-symmetric), border (regular to irregular), texture (smooth to rough). Using a web based form we asked people to pick a value of each attribute for each lesion. In the second part, we extract 93 features from each lesions and we trained a machine learning algorithm using such features as input and the values of the human attributes as output. Results are quite promising, especially for the colour related attributes, where our system classifies over 80% of the lesions into the same semantic classes as humans.
2010 by The University of Edinburgh. All Rights Reserved
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Bibtex format
author = { Nikolaos Lascaris and Lucia Ballerini and Robert Fisher and Ben Aldridge and Jonathan Rees },
title = {Fuzzy Description of Skin Lesions},
book title = {SPIE Medical Imaging 2010},
publisher = {SPIE},
year = 2010,

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