Gerard Lacey, & Fernando Vilariño. (2011). Endoscopy system with motion sensors.
Abstract: An endoscopy system (1) comprises an endoscope (2) with a camera (3) at its tip. The endoscope extends through an endoscope guide (4) for guiding movement of the endoscope and for measurement of its movement as it enters the body. The guide (4) comprises a generally conical body (5) having a through passage (105) through which the endoscope (2) extends. A motion sensor comprises an optical transmitter (7) and a detector (8) mounted alongside the passage (105) to measure the insertion-withdrawal linear motion and also rotation of the endoscope by the endoscopist's hand. The system (1) also comprises a flexure controller (10) having wheels operated by the endoscopist. The camera (3), the motion sensor (7/8), and the flexure controller (10) are all connected to a processor (11) which feeds a display.
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Farhan Riaz, Fernando Vilariño, Mario Dinis-Ribeiro, & Miguel Coimbraln. (2011). Identifying Potentially Cancerous Tissues in Chromoendoscopy Images. In and M. Hernandez J. M. S. J. Vitria (Ed.), 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 709–716). LNCS. Berlin: Springer.
Abstract: The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG filters generally give best classification accuracy among the other feature extraction methods considered.
Keywords: Endoscopy, Computer Assisted Diagnosis, Gradient.
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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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Fernando Vilariño, Panagiota Spyridonos, Fosca De Iorio, Jordi Vitria, Fernando Azpiroz, & Petia Radeva. (2010). Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions. TMI - IEEE Transactions on Medical Imaging, 29(2), 246–259.
Abstract: 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.
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Jorge Bernal, Fernando Vilariño, & F. Javier Sanchez. (2010). Feature Detectors and Feature Descriptors: Where We Are Now (Vol. 154).
Abstract: Feature Detection and Feature Description are clearly nowadays topics. Many Computer Vision applications rely on the use of several of these techniques in order to extract the most significant aspects of an image so they can help in some tasks such as image retrieval, image registration, object recognition, object categorization and texture classification, among others. In this paper we define what Feature Detection and Description are and then we present an extensive collection of several methods in order to show the different techniques that are being used right now. The aim of this report is to provide a glimpse of what is being used currently in these fields and to serve as a starting point for future endeavours.
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Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2010). Reduction of Pattern Search Area in Colonoscopy Images by Merging Non-Informative Regions. In 28th Congreso Anual de la Sociedad Española de Ingeniería Biomédica.
Abstract: One of the first usual steps in pattern recognition schemas is image segmentation, in order to reduce the dimensionality of the problem and manage smaller quantity of data. In our case as we are pursuing real-time colon cancer polyp detection, this step is crucial. In this paper we present a non-informative region estimation algorithm that will let us discard some parts of the image where we will not expect to find colon cancer polyps. The performance of our approach will be measured in terms of both non-informative areas elimination and polyps’ areas preserving. The results obtained show the importance of having correct non- informative region estimation in order to fasten the whole recognition process.
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Fernando Vilariño, Panagiota Spyridonos, Petia Radeva, Jordi Vitria, Fernando Azpiroz, & Juan Malagelada. (2010). Method for automatic classification of in vivo images.
Abstract: A method for automatically detecting a post-duodenal boundary in an image stream of the gastrointestinal (GI) tract. The image stream is sampled to obtain a reduced set of images for processing. The reduced set of images is filtered to remove non-valid frames or non-valid portions of frames, thereby generating a filtered set of valid images. A polar representation of the valid images is generated. Textural features of the polar representation are processed to detect the post-duodenal boundary of the GI tract.
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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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Jorge Bernal. (2009). Use of Projection and Back-projection Methods in Bidimensional Computed Tomography Image Reconstruction (Vol. 141). Master's thesis, , Barcelona, Spain.
Abstract: One of the biggest drawbacks related to the use of CT scanners is the cost (in memory and in time) associated. In this project many methods to simulate their functioning, but in a more feasible way (taking an industrial point of view), will be studied.
The main group of techniques that are being used are the one entitled as ’back-projection’. The concept behind is to simulate the X ray emission in CT scans by lines that cross with the image we want to reconstruct.
In the first part of this document euclidean geometry is used to face the tasks of projec- tion and back-projection. After analysing the results achieved it has been proved that this approach does not lead to a fully perfect reconstruction (and also has some other problems related to running time and memory cost). Because of this in the second part of the document ’Filtered Back-projection’ method is introduced in order to improve the results.
Filtered Back-projection methods rely on mathematical transforms (Fourier, Radon) in order to provide more accurate results that can be obtained in much less time. The main cause of this better results is the use of a filtering process before the back-projection in order to avoid high frequency-caused errors.
As a result of this project two different implementations (one for each approach) had been implemented in order to compare their performance.
Keywords: Projection, Back-projection, CT scan, Euclidean geometry, Radon transform
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Fernando Vilariño, Panagiota Spyridonos, Petia Radeva, Jordi Vitria, Fernando Azpiroz, & Juan Malagelada. (2009). Device, system and method for measurement and analysis of contractile activity.
Abstract: A method and system for determining intestinal dysfunction condition are provided by classifying and analyzing image frames captured in-vivo. The method and system also relate to the detection of contractile activity in intestinal tracts, to automatic detection of video image frames taken in the gastrointestinal tract including contractile activity, and more particularly to measurement and analysis of contractile activity of the GI tract based on image intensity of in vivo image data.
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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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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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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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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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