Automated Marsh-like classification of celiac disease in children using local texture operators

Automated classification of duodenal texture patches with histological ground truth in case of pediatric celiac disease is proposed. The classical focus of classification in this context is a two-class problem: mucosa affected by celiac disease and unaffected duodenal tissue. We extend this focus and apply classification according to a modified Marsh scheme into four classes. In addition to other techniques used previously for classification of endoscopic imagery, we apply local binary pattern (LBP) operators and propose two new operator types, one of which adapts to the different properties of wavelet transform subbands. The achieved results are promising in that operators based on LBP turn out to achieve better results compared to many other texture classification techniques as used in earlier work. Specifically, the proposed wavelet-based LBP scheme achieved the best overall accuracy of all feature extraction techniques considered in the two-class case and was among the best in the four-class scheme. Results also show that a classification into four classes is feasible in principle however when compared to the two-class case we note that there is still room for improvement due to various reasons discussed.