Pattern classification approach to segmentation of digital chest radiographs and chest CT image slices
Michael F. McNitt‐Gray, James William Sayre, H. K. Huang, Mahmood K. Razavi, Denise R. Aberle · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
The goal of this research was to develop a segmentation method based on a pattern classification approach. The pattern classification approach consists of classifying each pixel into one of several anatomic classes on the basis of one or more feature values. In this research, three types of locally calculated features are used: gray-level based measures, local difference measures and local texture measures. A feature selection process is performed to determine which features best discriminate between the anatomic classes. Three classifiers are used: a linear discriminant function, a k-nearest neighbor approach and a neural network. Supervised techniques train each classifier to learn the characterstics of the anatomic classes. Each classifier is trained and tested using normal images. The pattern classification approach to image segmentation has shown promise for further development. Locally calculated features are important in classifying pixels, but these alone may not be sufficient. A method for incorporating spatial information into the classification decision appears to improve the results and may be necessary for reliable segmentation. This research also shows that the pattern classification approach may be applied to images from different modalities.