Pulmonary CT image classification using evolutionary programming

M T Madsen; R Uppaluri; E A Hoffman; G McLennan

doi:10.1109/NSSMIC.1997.670520

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Conference proceeding

Pulmonary CT image classification using evolutionary programming

M T Madsen, R Uppaluri, E A Hoffman and G McLennan

1997 IEEE Nuclear Science Symposium Conference Record

11/1997

DOI: 10.1109/NSSMIC.1997.670520

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Abstract

In this paper, we report on the use of evolutionary programming for classifying lung CT images. Evolutionary programming uses a genetic algorithm to generate a complete, compilable program that optimizes a solution to set of training data. In this case, the training set consisted of 17 features derived from multiple lung CT images along with an indicator of the target area from which the features originated. The image features included 5 parameters based on histogram analysis, 11 parameters based on run length and co-occurrence matrix measures, and the fractal dimension. Evolutionary programming produced solutions that compared favorably with more complicated and sophisticated Bayesian classifiers. The results of this study suggest that evolutionary programming is a powerful tool for developing classification algorithms.

Computer Programming

Genetic Algorithms

Medical Imaging

Pulmonary Diseases

Feature extraction

Fractals

Image analysis

Details

Title: Subtitle: Pulmonary CT image classification using evolutionary programming
Creators: M T Madsen
R Uppaluri
E A Hoffman
G McLennan
Resource Type: Conference proceeding
Publication Details: 1997 IEEE Nuclear Science Symposium Conference Record
DOI: 10.1109/NSSMIC.1997.670520
ISSN: 1082-3654
Language: English
Date published: 11/1997
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984318792802771

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