Prostate cancer is one of the types of cancer with the highest incidence in humans. In particular, prostate cancer is the main cause of death from cancer in men over 70 years of age. The automatic analysis of histological images is nowadays a key factor for helping doctors in the diagnosis task. In this paper, we present granulometries as a novel image descriptor to identify abnormal patterns in the prostatic tissue. The morphological alteration suffered by the main structures of pathological glands are registered by the proposed descriptor and achieved in a feature vector. A committee of SVM classifiers is trained making use of the extracted information with the aim of discriminating between healthy and pathological tissue. The performance of the proposed image descriptor is validated in 45 images provided by the Hospital Clínico of Valencia. Accuracy, sensitivity, specificity and AUC values higher than 0.95+-0.02 demonstrate the effectiveness of the method.