Adaptive gray level run length features from class distance matrices

We constructed class distance matrices for the gray level run length texture analysis method. For a four-class problem of liver cell nuclei, we found that there exist areas of consistently high values in the class distance matrices. We combined the information from the entries of the normalized run length matrix, based on the class distance matrices, to obtain adaptive features for texture classification. Using this procedure, we extracted only two features, which halved the classification error when compared to the best pair of classical gray level run length matrix features