NDLI: Objective and cost-efficient approach for skin lesions classification

Content Provider	IEEE Xplore Digital Library
Author	Zagrouba, E. Barhoumi, W.
Copyright Year	2003
Description	Author affiliation: Departement des Sci. de l'informatique, Faculte des Sci. de Tunis, Tunisia (Zagrouba, E.)
Abstract	Summary form only given. We deal with developing methods for an objective and cost-efficient tool for diagnosing skin lesions based on digitized dermatoscopic color images. We define a segmentation approach by fuzzy region growing applied to the Karhunen-Loeve transform of the RGB color vectors to separate pigmented lesion from the surrounding healthy skin. Then, a set of 14 characteristics of the lesion, represented by a set of numbers called feature scores, is extracted from the binary mask of the lesion deduced by the segmentation step. The quality of the features is evaluated by applying several feature selection methods in order to eliminate redundant information and accelerate the further classification step. Results show that most selection methods allow one to reduce the feature set to dimension five, which permits considerable reduction of calculation time, without significant loss of information. Feeding the selected features to a multilayer perception classifier allows us to generate a computerized diagnosis, suggesting whether the lesion is benign or malignant. With this approach, for reasonably balanced training/testing sets, we obtain above 77% correct classification of the malignant and benign tumors on real skin images.
Sponsorship	Arab Comput. Soc. IEEE Comput. Soc
File Size	37806
File Format	PDF
ISBN	0780379837
DOI	10.1109/AICCSA.2003.1227567
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-14
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Skin Lesions Cancer Color Image segmentation Karhunen-Loeve transforms Pigmentation Feature extraction Data mining Acceleration
Content Type	Text
Resource Type	Article

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