Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Texture Features in the Classification of Melanocytic Lesions
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern analysis of dermoscopic images based on Markov random fields
Pattern Recognition
Melanoma recognition using representative and discriminative kernel classifiers
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Methodological review: Computerized analysis of pigmented skin lesions: A review
Artificial Intelligence in Medicine
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In this paper we introduce the importance of scale invariance in properly discriminating some of the typical patterns found in melanocytic lesions, by dermatoscopic image analysis. Pattern discrimination is a necessary step before pattern irregularity (an indicator of malignancy) can be quantified. We propose a set of features that allows for the discrimination of such patterns even when they appear in different degrees of magnification. We show how an automated feature selection stage produces a preferred scale invariant set of features among non-invariant features, yielding the best classification rate for those features. The average correct classification rate for thethose features. The average correct classification rate for the five kinds of classified patterns rises up to 94%.