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Proceedings Paper

Pattern classification approach to segmentation of chest radiographs
Author(s): Michael F. McNitt-Gray; James W. Sayre; H. K. Huang; Mahmood Razavi
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Paper Abstract

In digital chest radiography, the goal of segmentation is to automatically and reliably identify anatomic regions such as the heart and lungs. Aids to diagnosis such as automated anatomic measurements, methods that enhance display of specific regions, and methods that search for disease processes, all depend on a reliable segmentation method. The goal of this research is to develop a segmentation method based on a pattern classification approach. A set of 17 chest images was used to train each of the classifiers. The trained classifiers were then tested of a different set of 16 chest images. The linear discriminant correctly classified greater than 70%, the k-nearest neighbor correctly classified greater than 70% and the neural network classified greater than 76% of the pixels from the test images. Preliminary results are favorable for this approach. Local features do provide much information, but further improvement is expected when addition information, such as location, can be incorporated.

Paper Details

Date Published: 14 September 1993
PDF: 11 pages
Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); doi: 10.1117/12.154500
Show Author Affiliations
Michael F. McNitt-Gray, UCLA School of Medicine (United States)
James W. Sayre, UCLA School of Medicine (United States)
H. K. Huang, Univ. of California/San Francisco (United States)
Mahmood Razavi, UCLA School of Medicine (United States)


Published in SPIE Proceedings Vol. 1898:
Medical Imaging 1993: Image Processing
Murray H. Loew, Editor(s)

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