On the Classification of Prostate Pathological Images Based on Gleason Score

Prostate cancer is one of the most frequent cancers caused in men and automated classification results which can be provided as objective references are of great significance. Here we present a study of classification of histological images of prostate based on both morphological features and textural features. At first we get two tissues of prostate cancer which including nuclei, lumen from the image, then a total of 40 morphological and textural features from each digitized images of histological prostate tissue specimens. After feature selection, a support vector machine (SVM) is used to classify the digitized histology slides into two classes: "Gleason score=7". In our experiments the SVM classifier achieving an accuracy of 90.67% within the training set and 74.81% within the test set, respectively.