Rjb. Giesen et al., CONSTRUCTION AND APPLICATION OF HIERARCHICAL DECISION TREE FOR CLASSIFICATION OF ULTRASONOGRAPHIC PROSTATE IMAGES, Medical & biological engineering & computing, 34(2), 1996, pp. 105-109
A non-parametric algorithm is described for the construction of a bina
ry decision tree classifier. This tree is used to correlate textural f
eatures, computed from ultrasonographic prostate images, with the hist
opathology of the imaged tissue. The algorithm consists of two parts;
growing and pruning. In the growing phase an optimal tree is grown, ba
sed on the concept of mutual information. After growing, the tree is p
runed by an alternating interaction of two data sets. Moreover, the st
ructure and performance of the constructed tree are compared to the re
sults using a slightly modified corresponding growing and pruning algo
rithm. The modified algorithm provides better retrospective and prospe
ctive classification results than the original algorithm. The use of t
he tree for automated cancer detection in ultrasonographic prostate im
ages results in retrospective and prospective accuracy of 77 . 9% and
72 . 3%, respectively. Using this tissue characterisation, a supportin
g tool is provided for the interpretation of transrectal ultrasonograp
hic images.