of prostate stromal tissue in histological images using Haralick descriptors and Local Binary Patterns

In this paper we presente a classication system that uses a combination of texture features from stromal regions: Haralick features and Local Binary Patterns (LBP) in wavelet domain. The system has ve steps for classication of the tissues. First, the stromal regions were detected and extracted using segmentation techniques based on thresholding and RGB colour space. Second, the Wavelet decomposition was applied in the extracted regions to obtain the Wavelet coecients. Third, the Haralick and LBP features were extracted from the coecients. Fourth, relevant features were selected using the ANOVA statistical method. The classication (fth step) was performed with Radial Basis Function (RBF) networks. The system was tested in 105 prostate images, which were divided into three groups of 35 images: normal, hyperplastic and cancerous. The system performance was evaluated using the area under the ROC curve and resulted in 0.98 for normal versus cancer, 0.95 for hyperplasia versus cancer and 0.96 for normal versus hyperplasia. Our results suggest that texture features can be used as discriminators for stromal tissues prostate images. Furthermore, the system was eective to classify prostate images, specially the hyperplastic class which is the most dicult type in diagnosis and prognosis.