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Proceedings Paper

Fuzzy description of skin lesions
Author(s): Nikolaos Laskaris; Lucia Ballerini; Robert B. Fisher; Ben Aldridge; Jonathan Rees
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Paper Abstract

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.

Paper Details

Date Published: 23 February 2010
PDF: 10 pages
Proc. SPIE 7627, Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment, 762717 (23 February 2010); doi: 10.1117/12.845294
Show Author Affiliations
Nikolaos Laskaris, The Univ. of Edinburgh (United Kingdom)
Lucia Ballerini, The Univ. of Edinburgh (United Kingdom)
Robert B. Fisher, The Univ. of Edinburgh (United Kingdom)
Ben Aldridge, The Univ. of Edinburgh (United Kingdom)
Jonathan Rees, The Univ. of Edinburgh (United Kingdom)


Published in SPIE Proceedings Vol. 7627:
Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment
David J. Manning; Craig K. Abbey, Editor(s)

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