Pattern classification approach to segmentation of chest radiographs

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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.

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