MR image classification algorithms for neurosurgical and stereotactic radiosurgical trajectory planning and...

Neurosurgical and stereotactic radiosurgical trajectory planning involves a phase of image data acquisition followed by localization of tissues to be avoided or crossed during the procedure. Success of the procedure is often assessed during follow-up by performing volumetric analysis of tissues of interest. Both localization and volumetric analysis can be facilitated by classifying the input data into different tissue types. This study compares classification results based on four different input image data: (1) MR data obtained with one TR and TE parameter combination, (2) MR data acquired with eight combinations of TR and TE parameters, (3) input data compressed into four principal components, and (4) the input data (3) together with first- and second-order texture features. The algorithms (a) to reduce the dimensionality using principal components, (b) to generate texture features, (c) to determine feature usefulness in classification using the Wilk's lambda criterion, and (d) to classify the input set are described. Classification results are found to be poor for input data 1 and much improved (qualitatively and quantatively) for input data 2. Input data 3 produces classification results (quantitatively and qualitatively) similar to that of 2. Input data 4 is found to produce quantitative results similar to those of 2 and 3, but the qualitative results show enhanced classification of some tissues and distortion of others.