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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Stefan Ameling; Stephan Wirth; Dietrich Paulus; Gerard Lacey; Fernando Vilariño |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Texture-based Polyp Detection in Colonoscopy |
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Conference Article |
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Year |
2009 |
Publication |
Proc. BILDVERARBEITUNG FÜR DIE MEDIZIN |
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800 |
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MV;SIAI |
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no |
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fernando @ fernando @ |
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2428 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Santiago Segui; Michal Drozdzal; Fernando Vilariño; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy |
Type |
Journal Article |
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Year |
2012 |
Publication |
IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
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Volume |
16 |
Issue |
6 |
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1341-1352 |
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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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1089-7771 |
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800 |
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MILAB; MV; OR;SIAI |
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no |
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Admin @ si @ SDV2012 |
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2124 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Santiago Segui; Laura Igual; Fernando Vilariño; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy |
Type |
Book Chapter |
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Year |
2008 |
Publication |
Computer Vision Systems. 6th International |
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5008 |
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251–260 |
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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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Santorini (Greece) |
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Springer-Verlag |
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Berlin Heidelberg |
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A. Gasteratos, M. Vincze, and J.K. Tsotsos |
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LNCS |
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978-3-540-79546-9 |
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800 |
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ICVS |
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OR; MV; MILAB; SIAI |
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no |
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BCNPCL @ bcnpcl @ SIV2008; IAM @ iam @ SIV2008 |
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962 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Rozenn Dhayot; Fernando Vilariño; Gerard Lacey |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Improving the Quality of Color Colonoscopy Videos |
Type |
Journal Article |
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Year |
2008 |
Publication |
EURASIP Journal on Image and Video Processing |
Abbreviated Journal |
EURASIP JIVP |
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139429 |
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1 |
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1-9 |
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800 |
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MV;SIAI |
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no |
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Call Number |
fernando @ fernando @ |
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2422 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions |
Type |
Book Chapter |
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Year |
2006 |
Publication |
9th International Conference on Medical Image Computing and Computer–Assisted Intervention |
Abbreviated Journal |
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Volume |
4191 |
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161–168 |
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Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential 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 con- traction 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. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing 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. |
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Copenhagen (Denmark) |
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Springer Verlag |
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Berlin Heidelberg |
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R. Larsen, M. Nielsen, and J. Sporring |
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800 |
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MICCAI06 |
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MV;OR;MILAB;SIAI |
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no |
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BCNPCL @ bcnpcl @ SVV2006; IAM @ iam @ SVV2006 |
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725 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Mirko Arnold; Stephan Ameling; Anarta Ghosh; Gerard Lacey |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Quality Improvement of Endoscopy Videos |
Type |
Conference Article |
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2011 |
Publication |
Proceedings of the 8th IASTED International Conference on Biomedical Engineering |
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723 |
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800 |
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MV |
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no |
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fernando @ fernando @ |
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2426 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Mirko Arnold; Anarta Ghosh; Stephen Ameling; G Lacey |
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Title |
Automatic segmentation and inpainting of specular highlights for endoscopic imaging |
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Journal Article |
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2010 |
Publication |
EURASIP Journal on Image and Video Processing |
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EURASIP JIVP |
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2010 |
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9 |
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800 |
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MV |
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no |
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fernando @ fernando @ |
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2423 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Mirko Arnold; Anarta Ghosh; Glen Doherty; Hugh Mulcahy; Stephen Patchett; Gerard Lacey |
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Title |
Towards Automatic Direct Observation of Procedure and Skill (DOPS) in Colonoscopy |
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Conference Article |
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Year |
2013 |
Publication |
Proceedings of the International Conference on Computer Vision Theory and Applications |
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48-53 |
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VISIGRAPP |
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MV |
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no |
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fernando @ fernando @ |
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2427 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Mirko Arnold; Anarta Ghosh; Gerard Lacey; Stephen Patchett; Hugh Mulcahy |
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Title |
Indistinct frame detection in colonoscopy videos |
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Conference Article |
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2009 |
Publication |
Machine Vision and Image Processing Conference |
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47-52 |
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MV |
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no |
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fernando @ fernando @ |
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2424 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jorge Bernal; Fernando Vilariño; F. Javier Sanchez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Feature Detectors and Feature Descriptors: Where We Are Now |
Type |
Report |
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Year |
2010 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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154 |
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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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800 |
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MV;SIAI |
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no |
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Admin @ si @ BVS2010; IAM @ iam @ BVS2010 |
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1348 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jorge Bernal; Fernando Vilariño; F. Javier Sanchez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Towards Intelligent Systems for Colonoscopy |
Type |
Book Chapter |
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Year |
2011 |
Publication |
Colonoscopy |
Abbreviated Journal |
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Volume |
1 |
Issue |
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257-282 |
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In this chapter we present tools that can be used to build intelligent systems for colonoscopy.
The idea is, by using methods based on computer vision and artificial intelligence, add significant value to the colonoscopy procedure. Intelligent systems are being used to assist in other medical interventions |
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Intech |
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Paul Miskovitz |
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978-953-307-568-6 |
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800 |
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MV;SIAI |
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no |
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IAM @ iam @ BVS2011 |
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1697 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Reduction of Pattern Search Area in Colonoscopy Images by Merging Non-Informative Regions |
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Conference Article |
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2010 |
Publication |
28th Congreso Anual de la Sociedad Española de Ingeniería Biomédica |
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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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Madrid (Spain) |
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800 |
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CASEIB |
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MV;SIAI |
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no |
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Admin @ si @ BSV2010 |
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1469 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach |
Type |
Conference Article |
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Year |
2011 |
Publication |
2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems |
Abbreviated Journal |
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62-71 |
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Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time. |
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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. |
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Rome, Italy |
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SciTePress |
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Djemal, Khalifa |
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800 |
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MIAD |
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MV;SIAI |
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no |
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IAM @ iam @ BSV2011a |
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1695 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A Region Segmentation Method for Colonoscopy Images Using a Model of Polyp Appearance |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
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134-143 |
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Colonoscopy, Polyp Detection, Region Merging, Region Segmentation. |
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Abstract |
This work aims at the segmentation of colonoscopy images into a minimum number of informative regions. Our method performs in a way such, if a polyp is present in the image, it will be exclusively and totally contained in a single region. This result can be used in later stages to classify regions as polyp-containing candidates. The output of the algorithm also defines which regions can be considered as non-informative. The algorithm starts with a high number of initial regions and merges them taking into account the model of polyp appearance obtained from available data. The results show that our segmentations of polyp regions are more accurate than state-of-the-art methods. |
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Las Palmas de Gran Canaria, June 2011 |
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Vitrià, Jordi and Sanches, João and Hernández, Mario |
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Lecture Notes in Computer Science |
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978-3-642-21256-7 |
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800 |
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IbPRIA |
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MV;SIAI |
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Call Number |
IAM @ iam @ BSV2011c |
Serial |
1696 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Integration of Valley Orientation Distribution for Polyp Region Identification in Colonoscopy |
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Conference Article |
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Year |
2011 |
Publication |
In MICCAI 2011 Workshop on Computational and Clinical Applications in Abdominal Imaging |
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6668 |
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76-83 |
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Abstract |
This work presents a region descriptor based on the integration of the information that the depth of valleys image provides. The depth of valleys image is based on the presence of intensity valleys around polyps due to the image acquisition. Our proposed method consists of defining, for each point, a series of radial sectors around it and then accumulates the maxima of the depth of valleys image only if the orientation of the intensity valley coincides with the orientation of the sector above. We apply our descriptor to a prior segmentation of the images and we present promising results on polyp detection, outperforming other approaches that also integrate depth of valleys information. |
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Toronto, Canada |
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Lecture Notes in Computer Science |
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800 |
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ABI |
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Notes |
MV;SIAI |
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Call Number |
IAM @ iam @ BSV2011d |
Serial |
1698 |
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