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Proceedings Paper

Visual recognition of complex medical lesions using 2D shape
Author(s): Artur Chodorowski; Tomas Gustavsson; Ulf Mattsson
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Paper Abstract

Different shape representation and classification methods for complex medical lesions were compared using oral lesions as a case study. The problem studied was the discrimination between potentially cancerous lesions, called leukoplakia, and other usually harmless lesions, called lichenoid reactions, which can appear in human oral cavities. The classification problem is difficult because these lesions vary in shape within classes and there are no easily recognizable characteristics. The representations evaluated were the centroidal profile function, the curvature function, and polar and complex coordinate functions. From these representations, translation, scale and rotation independent features were derived using Fourier transformations, auto-regressive modeling, and Zernike moments. A nonparametric kNN classifier with the leave-one-out cross-validation method was used as a classifier. An overall classification accuracy of about 84% was achieved using only the shape properties of the lesions, compared with a human visual classification rate of 65%. The best results were obtained using complex representation and Fourier/Zernike methods. In clinical practice, the preliminary diagnosis is based mainly on the visual inspection of the oral cavity, using both color, shape and texture as differentiating parameters. This study showed that machine analysis of shape could also play an important part in diagnosis and decisions regarding future treatment.

Paper Details

Date Published: 2 June 2000
PDF: 10 pages
Proc. SPIE 3959, Human Vision and Electronic Imaging V, (2 June 2000); doi: 10.1117/12.387166
Show Author Affiliations
Artur Chodorowski, Chalmers Univ. of Technology (Sweden)
Tomas Gustavsson, Chalmers Univ. of Technology (Sweden)
Ulf Mattsson, Central Hospital/Karlstad (Sweden)

Published in SPIE Proceedings Vol. 3959:
Human Vision and Electronic Imaging V
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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