Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonie mucosa

The development of an automated algorithm for the categorization of normal and cancerous colon mucosa is reported. Six features based on texture analysis were studied. They were derived using the co-occurrence matrix and were angular second moment entropy contrast inverse difference moment dissimilarity and correlation. Optical density was also studied. Forty-four normal images and 58 cancerous images from sections of the colon were analyzed. These two groups were split equally into two subgroups: one set was used for supervised training and the other to test the classification algorithm. A step wise selection procedure showed that correlation and entropy were the features that discriminated most strongly between normal and cancerous tissue (P < 0.0001). A parametric linear-discriminate function was used to determine the classification rule. For the training set a sensitivity and specificity of 93.1% and 81.8% respectively were achieved with an overall accuracy of 88.2%. These results were confirmed with the test set with a sensitivity and specificity of 93.1% and 86.4% respectively and an overall accuracy of 90.2%.