Colour texture analysis using co-occurrence matrices for classification of colon cancer images

Worldwide, colorectal cancer is the third most common malignant neoplasm. Automated classification of cytological images of colon tissue samples has been investigated, but diagnosis in all cases still requires human judgement. With the large numbers of cases of colon cancer each year, the workload placed on pathologists is immense. Texture is a powerful discriminating metric and the use of grey-level texture for classification of colon images has been extensively researched. One common technique is the extraction of texture metrics from grey-level co-occurrence matrices. However, using grey-scale images discards information contained in the differences of hue and saturation that may provide further classification information. We present the findings of an investigation of the discriminating ability of colour texture using co-occurrence matrices. Comparisons are made between grey-scale and colour texture analysis. Using statistical analysis, we show that classification using colour texture offers an improvement over classification based solely on grey-level texture.