Segmentation and visualization of multispectral medical images with interactive control of parameters for a set of unsupervised classifiers
Eileen M. McMahon, Armando Manduca, Richard A. Robb · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Multispectral classification uses registered 3-D image volumes from more than one imaging modality or from different sequences within a modality to classify tissues within those volumes. The complementary information contained within the different image volumes may allow for the separation of tissue class types in multidimensional feature space when the same tissue classes would be indistinct using just one image volume. When segmentation is complete, attributes of these classes may be determined (e.g., volumes), or the classes may be visualized as objects in 3-D. There are two main types of classification algorithms: supervised and unsupervised. Unsupervised classifiers offer the promise of totally automated classification of tissue types and calculation of tissue volumes and other tissue properties in medical images. This would have two benefits: (1) elimination of the time-consuming process of manual segmentation by medical experts, and (2) ensuring reproducible results. While accurate performance by unsupervised classifiers is, in general, still impossible, an intermediate step is the development of tools to allow users to obtain useful results in a relatively short period of time. This paper describes such a tool which allows users to quickly and easily experiment with various choices of unsupervised classification algorithms and their input parameters and evaluate the results.