Cardinal Multiridgelet-based Prostate Cancer Histological Image Classification for Gleason Grading

Computer-aided Gleason grading of prostate cancer tissue images has been in rapid development during the past decade. Automated classifiers using features derived from multiwavelet transform, fractal dimension and other measurements, and using texton forests have shown considerable successes. This paper presents our study on application of cardinal multiridgelet transform (CMRT) to prostate cancer images to extract texture features in the transform domain. CMRT can provide cardinality, approximate translation invariance and rotation invariance simultaneously. With 32 images of Gleason grade 3 and grade 4 as a training set and using texture features extracted therefrom, a support vector machine with Gaussian kernel has been trained to classify grade 3 and grade 4. The leave-one-out cross-validation showed its accuracy of 93.75% and AUC of 0.9651. 10 test images of grade 4 showed 100% accuracy.