Abstract: A computer diagnostic system and related method are disclosed for automatically classifying tissue types in an original tissue image captured by an imaging device based on texture analysis. In one embodiment, the system receives and divides the tissue image into multiple smaller tissue block images. A combination of local binary pattern (LBP), average LBP (ALBP), and block-based LBP (BLBP) feature extractions are performed on each tissue block. The extractions generate a set of LBP, ALBP, and BLBP features for each block which are used to classify its tissue type. The classification results are visually displayed in a digitally enhanced map of the original tissue image. In one embodiment, a tissue type of interest is displayed in the original tissue image. In another or the same embodiment, the map displays each of the different tissue types present in the original tissue image.

Abstract: A computer diagnostic system and related method are disclosed for automatically classifying tissue types in an original tissue image captured by an imaging device based on texture analysis. In one embodiment, the system receives and divides the tissue image into multiple smaller tissue block images. A combination of local binary pattern (LBP), average LBP (ALBP), and block-based LBP (BLBP) feature extractions are performed on each tissue block. The extractions generate a set of LBP, ALBP, and BLBP features for each block which are used to classify its tissue type. The classification results are visually displayed in a digitally enhanced map of the original tissue image. In one embodiment, a tissue type of interest is displayed in the original tissue image. In another or the same embodiment, the map displays each of the different tissue types present in the original tissue image.