Multispectral texture analysis of histopathological abnormalities in colorectal tissues

摘要

This paper proposes to use texture features extracted from multispectral microscopic images to detect histopathological abnormalities related to colorectal cancer (CRC): stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) and carcinoma (Ca). Texture features, based on gray-level co-occurrence matrices (GLCM) and discrete wavelets (DW), are obtained from colon biopsy images, captured using 16 different bands of the visible spectrum. A random forest classifier is used to evaluate the usefulness of these texture features, for each spectral band, on the task of discriminating between the four types of abnormal tissue. Preliminary results on the data of 39 CRC patients show that such features, in particular those based on GLCM and Symlet wavelets, can accurately predict the type of CRC tissue (94% accuracy, 88% sensibility and 100% specificity for Symlet features in the 16th spectral band). These results also reveal important differences in the textural information captured in each band, which could be used to develop more efficient procedures for the diagnosis of CRC.