Construction and application of hierarchical decision tree for classification of ultrasonographic prostate images.

A non-parametric algorithm is described for the construction of a binary decision tree classifier. This tree is used to correlate textural features, computed from ultrasonographic prostate images, with the histopathology of the imaged tissue. The algorithm consists of two parts; growing and pruning. In the growing phase an optimal tree is grown, based on the concept of mutual information. After growing, the tree is pruned by an alternating interaction of two data sets. Moreover, the structure and performance of the constructed tree are compared to the results using a slightly modified corresponding growing and pruning algorithm. The modified algorithm provides better retrospective and prospective classification results than the original algorithm. The use of the tree for automated cancer detection in ultrasonographic prostate images results in retrospective and prospective accuracy of 77.9% and 72.3%, respectively. Using this tissue characterisation, a supporting tool is provided for the interpretation of transrectal ultrasonographic images.