Abstract
Several image classification problems are handled using a classical statistical pattern recognition methodology: image segmentation, visual feature extraction, classification. The accuracy of the solution is typically measured by comparing automatic results with manual classification ones, where the distinction between these three steps is not clear at all. In this paper we will focus on one of these steps by addressing the following question: does the visual relevance exploited by segmentation algorithms reflect the semantic relevance of the manual annotation performed by the user? For this purpose we chose a gastroenterology scenario where clinicians classified a set of images into three different types (cancer, pre-cancer, normal), and manually segmented the area they believe was responsible for this classification. Afterwards, we have quantified the performance of two popular segmentation algorithms (mean shift, normalized cuts) on how well they produced one image patch that approximates manual annotation. Results showed that, for this case study, this resemblance is quite close for a large percentage of the images when using normalized cuts.

Abstract
Endoscopic capsule is a recent medical technology with important clinical benefits but suffering from a practical handicap: long exam annotation times. This paper proposes and compares two approaches (Bayesian and support vector machines) that can be used to segment the gastrointestinal tract into its four major topographic areas, allowing the automatic estimation of the clinically relevant gastric and intestinal sections and corresponding transit times. According to medical specialists, this can reduce exam annotation times by up to 12% (15 min). This automatic tool has been integrated into our CapView annotation software that is currently being used by three medical institutions.

Abstract
Recent advances in miniaturization led to the development of what is now called the endoscopic capsule. This small device is swallowed by a patient ana films the whole gastrointestinal tract, allowing the detection of abnormalities. Currently, a doctor typically needs up to two hours to analyze a full exam, so automation is desirable. This paper presents a methodology for measuring the potential of selected visual MPEG-7 descriptors for the task of specific medical event detection such as blood, ulcers. Experiments show that the best results are obtained by the Scalable Color and Homogenous Texture descriptors, especially if only relevant coefficients are used.

Abstract
Automatic classification of lesions for gastroenterology imaging scenarios poses novel challenges to computer-assisted decision systems, which are mostly attributed to the dynamics of the image acquisition conditions. Such challenges demand that automatic systems are able to give robust characterizations of tissues irrespective of camera rotation, zoom, and illumination gradients when viewing the inner surface of the gastrointestinal tract. In this paper, we study the invariance properties of Gabor filters and propose a novel descriptor, the autocorrelation Gabor features (AGF). We show that our proposed AGF is invariant to scale, rotation, and illumination changes in the images. We integrate these new features in a texton framework (Texton-AGF) to classify images from two complementary gastroenterology imaging scenarios (chromoendoscopy and narrow-band imaging) broadly into three different groups: normal, precancerous, and cancerous. Results show that they compare favorably to using state-of-the-art texture descriptors for both imaging modalities.

Abstract
Automatic classification of cancer lesions for gastroenterology imaging scenarios poses novel challenges to computer assisted decision systems, owing to their distinct visual characteristics such as reduced color spaces or natural organic textures. In this paper, we explore the prospects of using Gabor filters in a texton framework for the classification of images from two distinct imaging modalities (chromoendoscopy and narrow-band imaging) into three different groups: normal, precancerous and cancerous. Results show that they produce consistent results for both imaging modalities, hinting on their possible generic use for the classification of in-body images.

Abstract
In-body imaging technologies such as vital-stained magnification endoscopy pose novel image processing challenges to computer-assisted decision systems given their unique visual characteristics such as reduced color spaces and natural textures. In this paper we will show the potential of using adapted color features combined with local binary patterns, a texture descriptor that has exhibited good adaptation to natural images, for classifying gastric regions into three groups: normal, pre-cancer and cancer lesions. Results exhibit 91% accuracy, confirming that specific research for in-body imaging could be the key for future computer assisted decision systems for medicine.