Visual recognition of complex medical lesions using 2D shape

Artur Chodorowski; Tomas Gustavsson; Ulf Mattsson

doi:10.1117/12.387166

2 June 2000 Visual recognition of complex medical lesions using 2D shape

Artur Chodorowski, Tomas Gustavsson, Ulf Mattsson

Proceedings Volume 3959, Human Vision and Electronic Imaging V; (2000) https://doi.org/10.1117/12.387166
Event: Electronic Imaging, 2000, San Jose, CA, United States

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.

Citation Download Citation

Artur Chodorowski, Tomas Gustavsson, and Ulf Mattsson "Visual recognition of complex medical lesions using 2D shape", Proc. SPIE 3959, Human Vision and Electronic Imaging V, (2 June 2000); https://doi.org/10.1117/12.387166

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Autoregressive models

Resolution enhancement technologies

Visualization

Error analysis

Cancer

Fourier transforms

Classification systems

Show All Keywords

Keywords/Phrases

Search In:

Publication Years