In diagnosing celiac disease (CD), serological tests are highly valuable. However, their role in following up children with CD after prescription of a gluten-free diet is unclear. This study aimed to compare the performance of antibody tests in predicting small-intestinal mucosal status in diagnosis vs. follow-up of pediatric CD.

In this work we propose a method to extract shape-based features from endoscopic images for an automated classification of colonic polyps. This method is based on the density of pits as used in the pit pattern classification scheme which is commonly used for the classification of colonic polyps. For the detection of pits we employ a noise-robust variant of the LBP operator. To be able to be robust against local texture variations we extend this operator by an adaptive thresholding. Based on the detected pit candidates we compute a Delaunay triangulation and use the edge lengths of the resulting triangles to construct histograms. These are then used in conjunction with the k-NN classifier to classify images. We show that, compared to a previously developed method, we are not only able to almost always get higher classification results in our application scenario, but that the proposed method is also able to significantly outperform the previously developed method in terms of the computational demand.

Celiac disease (CD) is a complex autoimmune disorder in genetically predisposed individuals of all age groups triggered by the ingestion of food containing gluten. A reliable diagnosis is of high interest in view of embarking on a strict gluten-free diet, which is the CD treatment modality of first choice. The gold standard for diagnosis of CD is currently based on a histological confirmation of serology, using biopsies performed during upper endoscopy. Computer aided decision support is an emerging option in medicine and endoscopy in particular. Such systems could potentially save costs and manpower while simultaneously increasing the safety of the procedure. Research focused on computer-assisted systems in the context of automated diagnosis of CD has started in 2008. Since then, over 40 publications on the topic have appeared. In this context, data from classical flexible endoscopy as well as wireless capsule endoscopy (WCE) and confocal laser endomicrosopy (CLE) has been used. In this survey paper, we try to give a comprehensive overview of the research focused on computer-assisted diagnosis of CD.

Convolutional neural networks (CNNs) are gaining more and more interest in computer vision. CNNs widely replaced “shallow” (nondeep learning based) image representations, such as Fisher vectors, vector of locally aggregated descriptors (VLAD), or the bag-of-words (BoW) approach, which were previously state of the art.1 Also in case of the automated diagnosis of endoscopic images, CNNs outperformed shallow image representations.2,3

In this work we present a method for automated classification of endoscopic images according to the pit pattern classification scheme. Images taken during colonoscopy are transformed using a modified version of the local binary patterns operator (LBP). Then, two-dimensional histograms based on the LBP data from different color channels are created. Finally, the classification is carried out by employing the nearest-neighbors (1-NN) classifier in conjunction with the Bhattacharyya distance metric. The experimental results show that the extended LBP operator delivers superior results and an automated classification of endoscopic images based on the pit pattern classification scheme is feasible.

We present a system for an automated colon cancer detection based on the pit pattern classification. In contrast to previous work we exploit the visual nature of the underlying classification scheme by extracting features based on detected edges. To focus on the most discriminative subset of features we use a greedy forward feature subset selection. The classification is then carried out using the k-nearest neighbors (k-NN) classifier. The results obtained are very promising and show that an automated classification of the given imagery is feasible by using the proposed method.