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Santiago Segui, Michal Drozdzal, Fernando Vilariño, Carolina Malagelada, Fernando Azpiroz, Petia Radeva, et al. (2012). Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy. TITB - IEEE Transactions on Information Technology in Biomedicine, 16(6), 1341–1352.
Abstract: Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content – clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media.
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Joan M. Nuñez, Debora Gil, & Fernando Vilariño. (2013). Finger joint characterization from X-ray images for rheymatoid arthritis assessment. In 6th International Conference on Biomedical Electronics and Devices (pp. 288–292). SciTePress.
Abstract: In this study we propose amodular systemfor automatic rheumatoid arthritis assessment which provides a joint space width measure. A hand joint model is proposed based on the accurate analysis of a X-ray finger joint image sample set. This model shows that the sclerosis and the lower bone are the main necessary features in order to perform a proper finger joint characterization. We propose sclerosis and lower bone detection methods as well as the experimental setup necessary for its performance assessment. Our characterization is used to propose and compute a joint space width score which is shown to be related to the different degrees of arthritis. This assertion is verified by comparing our proposed score with Sharp Van der Heijde score, confirming that the lower our score is the more advanced is the patient affection.
Keywords: Rheumatoid Arthritis; X-Ray; Hand Joint; Sclerosis; Sharp Van der Heijde
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Joan M. Nuñez, Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2013). Blood Vessel Characterization in Colonoscopy Images to Improve Polyp Localization. In Proceedings of the International Conference on Computer Vision Theory and Applications (Vol. 1, pp. 162–171). SciTePress.
Abstract: This paper presents an approach to mitigate the contribution of blood vessels to the energy image used at different tasks of automatic colonoscopy image analysis. This goal is achieved by introducing a characterization of endoluminal scene objects which allows us to differentiate between the trace of 2-dimensional visual objects,such as vessels, and shades from 3-dimensional visual objects, such as folds. The proposed characterization is based on the influence that the object shape has in the resulting visual feature, and it leads to the development of a blood vessel attenuation algorithm. A database consisting of manually labelled masks was built in order to test the performance of our method, which shows an encouraging success in blood vessel mitigation while keeping other structures intact. Moreover, by extending our method to the only available polyp localization
algorithm tested on a public database, blood vessel mitigation proved to have a positive influence on the overall performance.
Keywords: Colonoscopy; Blood vessel; Linear features; Valley detection
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Fernando Vilariño, Stephan Ameling, Gerard Lacey, Stephen Patchett, & Hugh Mulcahy. (2009). Eye Tracking Search Patterns in Expert and Trainee Colonoscopists: A Novel Method of Assessing Endoscopic Competency? GI - Gastrointestinal Endoscopy, 69(5), 370.
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Rozenn Dhayot, Fernando Vilariño, & Gerard Lacey. (2008). Improving the Quality of Color Colonoscopy Videos. EURASIP JIVP - EURASIP Journal on Image and Video Processing, 139429(1), 1–9.
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Mirko Arnold, Anarta Ghosh, Stephen Ameling, & G Lacey. (2010). Automatic segmentation and inpainting of specular highlights for endoscopic imaging. EURASIP JIVP - EURASIP Journal on Image and Video Processing, 2010(9).
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Mirko Arnold, Anarta Ghosh, Gerard Lacey, Stephen Patchett, & Hugh Mulcahy. (2009). Indistinct frame detection in colonoscopy videos. In Machine Vision and Image Processing Conference (pp. 47–52).
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Mirko Arnold, Stephan Ameling, Anarta Ghosh, & Gerard Lacey. (2011). Quality Improvement of Endoscopy Videos. In Proceedings of the 8th IASTED International Conference on Biomedical Engineering (Vol. 723).
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Mirko Arnold, Anarta Ghosh, Glen Doherty, Hugh Mulcahy, Stephen Patchett, & Gerard Lacey. (2013). Towards Automatic Direct Observation of Procedure and Skill (DOPS) in Colonoscopy. In Proceedings of the International Conference on Computer Vision Theory and Applications (pp. 48–53).
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Stefan Ameling, Stephan Wirth, Dietrich Paulus, Gerard Lacey, & Fernando Vilariño. (2009). Texture-based Polyp Detection in Colonoscopy. In Proc. BILDVERARBEITUNG FÜR DIE MEDIZIN.
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Fernando Vilariño, & Gerard Lacey. (2009). QUALITY ASSESSMENT IN COLONOSCOPY New challenges through computer vision-based systems. In in Proc. 3rd International Conference on Biomedical Electronics and Devices.
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Fernando Vilariño, Gerard Lacey, Jiang Zhou, Hugh Mulcahy, & Stephen Patchett. (2007). Automatic Labeling of Colonoscopy Video for Cancer Detection. In In Proc. berian Conference, IbPRIA (pp. 290–297).
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Fernando Vilariño, Panagiota Spyridonos, Jordi Vitria, Carolina Malagelada, & Petia Radeva. (2006). Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions. In .F. Mart ́ınez-Trinidad et al (Ed.), 11th Iberoamerican Congress on Pattern Recognition (Vol. 4225, 178–187). LNCS. Berlin Heidelberg: Springer Verlag.
Abstract: 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.
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Santiago Segui, Laura Igual, Fernando Vilariño, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, et al. (2008). Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy. In and J.K. Tsotsos M. V. A. Gasteratos (Ed.), Computer Vision Systems. 6th International (Vol. 5008, 251–260). LNCS. Berlin Heidelberg: Springer-Verlag.
Abstract: Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are pro- vided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learn- ing framework in order to obtain the characterization of diverse motil- ity events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists.
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Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2011). Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach. In K. Djemal (Ed.), 2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems (pp. 62–71). SciTePress.
Abstract: In this paper we present our approach on real-time polyp detection in colonoscopy videos. Our method consists of three stages: Image Segmentation, Region Description and Image Classification. Taking into account the constraints of our project, we introduce our segmentation system that is based on the model of appearance of the polyp that we have defined after observing real videos from colonoscopy processes. The output of this stage will ideally be a low number of regions of which one of them should cover the whole polyp region (if there is one in the image). This regions will be described in terms of features and, as a result of a machine learning schema, classified based on the values that they have for the several features that we will use on their description. Although we are still on the early stages of the project, we present some preliminary segmentation results that indicates that we are going in a good direction.
Keywords: Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time.
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