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Pulmonary CT image classification with evolutionary programming
- Source :
- Academic Radiology. 6:736-741
- Publication Year :
- 1999
- Publisher :
- Elsevier BV, 1999.
-
Abstract
- Rationale and Objectives. It is often difficult to classify information in medical images from derived features. The purpose of this research was to investigate the use of evolutionary programming as a tool for selecting important features and generating algorithms to classify computed tomographic (CT) images of the lung. Materials and Methods. Training and test sets consisting of 11 features derived from multiple lung CT images were generated, along with an indicator of the target area from which features originated. The images included five parameters based on histogram analysis, 11 parameters based on run length and co-occurrence matrix measures, and the fractal dimension. Two classification experiments were performed. In the first, the classification task was to distinguish between the subtle but known differences between anterior and posterior portions of transverse lung CT sections. The second classification task was to distinguish normal lung CT images from emphysematous images. The performance of the evolutionary programming approach was compared with that of three statistical classifiers that used the same training and test sets. Results. Evolutionary programming produced solutions that compared favorably with those of the statistical classifiers. In separating the anterior from the posterior lung sections, the evolutionary programming results were better than two of the three statistical approaches. The evolutionary programming approach correctly identified all the normal and abnormal lung images and accomplished this by using less features than the best statistical method. Conclusion. The results of this study demonstrate the utility of evolutionary programming as a tool for developing classification algorithms.
- Subjects :
- Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Machine learning
computer.software_genre
Fractal dimension
Diagnostic aid
Computed tomographic
Artificial Intelligence
Histogram
Image Interpretation, Computer-Assisted
Humans
Radiology, Nuclear Medicine and imaging
Lung
Contextual image classification
business.industry
Pattern recognition
Statistical classification
ComputingMethodologies_PATTERNRECOGNITION
Pulmonary Emphysema
Normal lung
Artificial intelligence
Tomography, X-Ray Computed
business
computer
Algorithms
Evolutionary programming
Subjects
Details
- ISSN :
- 10766332
- Volume :
- 6
- Database :
- OpenAIRE
- Journal :
- Academic Radiology
- Accession number :
- edsair.doi.dedup.....51a1a9c75f2d865dae3e13ff424613d5