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Impact of endoscopic image degradations on LBP based features using one-class SVM for classification of celiac disease

The prevalence data of celiac disease have been continuously corrected upwards in the last years. An automated decision support system could improve the diagnosis and safety of the endoscopic procedure. An approach towards such a system is based on a one-class classifier (such as SVM) trained on celiac data only. By doing so, no special treatment of distorted image areas is needed. However, the performance of such a system is highly dependent on the discriminative power of the extracted features within an unconstrained environment such as the human bowel. Towards such a system we evaluate how well methods used in past work perform using a one-class SVM with images exhibiting common endoscopic image degradations such as blur, noise, light reflections and bubbles.