Panagiota Spyridonos

Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy

Wireless capsule video endoscopy is a novel and challenging clinical technique, whose major reported drawback relates to the high amount of time needed for video visualization. In this paper, we propose a method for the rejection of the parts of the video resulting not valid for analysis by means of automatic detection of intestinal juices. We applied Gabor filters for the characterization of the bubble-like shape of intestinal juices in fasting patients. Our method achieves a significant reduction in visualization time, with no relevant loss of valid frames. The proposed approach is easily extensible to other image analysis scenarios where the described pattern of bubbles can be found

Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions

Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions shown in a video provided by an ingestible capsule with a wireless micro-camera. The manual labeling of all the motility events requires large amount of time for offline screening in search of findings with low prevalence, which turns this procedure currently unpractical. In this paper, we propose a machine learning system to automatically detect the phasic intestinal contractions in video capsule endoscopy, driving a useful but not feasible clinical routine into a feasible clinical procedure. Our proposal is based on a sequential design which involves the analysis of textural, color, and blob features together with SVM classifiers. Our approach tackles the reduction of the imbalance rate of data and allows the inclusion of domain knowledge as new stages in the cascade. We present a detailed analysis, both in a quantitative and a qualitative way, by providing several measures of performance and the assessment study of interobserver variability. Our system performs at 70% of sensitivity for individual detection, whilst obtaining equivalent patterns to those of the experts for density of contractions.

Experiments with SVM and stratified sampling with an imbalanced problem: detection of intestinal contractions

In this paper we show some preliminary results of our research in the fieldwork of classification of imbalanced datasets with SVM and stratified sampling. Our main goal is to deal with the clinical problem of automatic intestinal contractions detection in endoscopic video images. The prevalence of contractions is very low, and this yields to highly skewed training sets. Stratified sampling together with SVM have been reported in the literature to behave well in this kind of problems. We applied both the SMOTE algorithm developed by Chawla et al. and under-sampling, in a cascade system implementation to deal with the skewed training sets in the final SVM classifier. We show comparative results for both sampling techniques using precision-recall curves, which appear to be useful tools for performance testing.

Anisotropic feature extraction from endoluminal images for detection of intestinal contractions

Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of contractions and to analyze the intestine motility. Feature extraction is essential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of contraction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Features extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belonging to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.

Identification of intestinal motility events of capsule endoscopy video analysis

In this paper we introduce a system for assisting the analysis of capsule-endoscopy (CE) data, and identifying sequences of frames related to small intestine motility. The imbalanced recognition task of intestinal contractions was addressed by employing an efficient two-level video analysis system. At the first level, each video was processed resulting in a number of possible sequences of contractions. In the second level, the recognition of contractions was carried out by means of a SVM classifier. To encode patterns of intestinal motility a panel of textural and morphological features of the intestine lumen were extracted. The system exhibited an overall sensitivity of 73.53% in detecting contractions. The false alarm ratio was of the order of 59.92%. These results serve as a first step for developing assisting tools for computer based CE video analysis, reducing drastically the physician’s time spent in image evaluation and enhancing the diagnostic potential of CE examination.

Linear radial patterns characterization for automatic detection of tonic intestinal contractions

This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns.

A machine learning framework using SOMs: applications in the intestinal motility assessment

Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment.