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Fuzzy Description of Skin Lesions

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    Rights statement: Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

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http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=748182
Original languageEnglish
Title of host publicationMEDICAL IMAGING 2010: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT
EditorsDJ Manning, CK Abbey
Place of PublicationBELLINGHAM
PublisherSPIE
Pages-
Number of pages10
ISBN (Print)978-0-8194-8028-6
DOIs
Publication statusPublished - 2010
EventConference on Image Perception, Observer Performance, and Technology Assessment - San Diego
Duration: 17 Feb 201018 Feb 2010

Conference

ConferenceConference on Image Perception, Observer Performance, and Technology Assessment
CitySan Diego
Period17/02/1018/02/10

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 from we asked people to pick a value of each attribute for cash 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.

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