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Author |
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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Year |
2009 |
Publication |
Machine Vision and Image Processing Conference |
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47-52 |
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800 |
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MV |
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no |
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Call Number |
fernando @ fernando @ |
Serial |
2424 |
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Author |
Mirko Arnold; Stephan Ameling; Anarta Ghosh; Gerard Lacey |
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Title |
Quality Improvement of Endoscopy Videos |
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Conference Article |
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Year |
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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Call Number |
fernando @ fernando @ |
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2426 |
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Author |
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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800 |
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VISIGRAPP |
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MV |
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no |
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Call Number |
fernando @ fernando @ |
Serial |
2427 |
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Author |
Stefan Ameling; Stephan Wirth; Dietrich Paulus; Gerard Lacey; Fernando Vilariño |
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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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Call Number |
fernando @ fernando @ |
Serial |
2428 |
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Author |
Fernando Vilariño; Gerard Lacey |
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Title |
QUALITY ASSESSMENT IN COLONOSCOPY New challenges through computer vision-based systems |
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Conference Article |
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Year |
2009 |
Publication |
in Proc. 3rd International Conference on Biomedical Electronics and Devices |
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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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2430 |
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Author |
Fernando Vilariño; Gerard Lacey; Jiang Zhou; Hugh Mulcahy; Stephen Patchett |
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Title |
Automatic Labeling of Colonoscopy Video for Cancer Detection |
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Conference Article |
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Year |
2007 |
Publication |
In Proc. berian Conference, IbPRIA |
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290-297 |
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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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2431 |
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Author |
Jorge Bernal |
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Title |
Use of Projection and Back-projection Methods in Bidimensional Computed Tomography Image Reconstruction |
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Report |
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Year |
2009 |
Publication |
CVC Tecnical Report |
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Volume |
141 |
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Keywords |
Projection, Back-projection, CT scan, Euclidean geometry, Radon transform |
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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. |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Barcelona, Spain |
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800 |
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MV; |
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Call Number |
IAM @ iam @ Ber2009 |
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1693 |
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Permanent link to this record |
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Author |
Fernando Vilariño |
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Title |
A Machine Learning Approach for Intestinal Motility Assessment with Capsule Endoscopy |
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Year |
2006 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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 obtained by labelling all the motility events present in a video provided by a capsule with a wireless micro-camera, which is ingested by the patient. However, the visual analysis of these video sequences presents several im- portant drawbacks, mainly related to both the large amount of time needed for the visualization process, and the low prevalence of intestinal contractions in video.
In this work we propose a machine learning system to automatically detect the intestinal contractions in video capsule endoscopy, driving a very useful but not fea- sible clinical routine into a feasible clinical procedure. Our proposal is divided into two different parts: The first part tackles the problem of the automatic detection of phasic contractions in capsule endoscopy videos. Phasic contractions are dynamic events spanning about 4-5 seconds, which show visual patterns with a high variability. Our proposal is based on a sequential design which involves the analysis of textural, color and blob features with powerful classifiers such as SVM. This approach appears to cope with two basic aims: the reduction of the imbalance rate of the data set, and the modular construction of the system, which adds the capability of including domain knowledge as new stages in the cascade. The second part of the current work tackles the problem of the automatic detection of tonic contractions. Tonic contrac- tions manifest in capsule endoscopy as a sustained pattern of the folds and wrinkles of the intestine, which may be prolonged for an undetermined span of time. Our proposal is based on the analysis of the wrinkle patterns, presenting a comparative study of diverse features and classification methods, and providing a set of appro- priate descriptors for their characterization. We provide a detailed analysis of the performance achieved by our system both in a qualitative and a quantitative way. |
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CVC (UAB) |
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Ph.D. thesis |
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Editor |
Petia Radeva |
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84-933652-7-0 |
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800 |
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MV;SIAI |
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no |
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Admin @ si @ Vil2006; IAM @ iam @ Vil2006 |
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738 |
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Permanent link to this record |
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Author |
Jorge Bernal |
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Title |
Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Colorectal cancer is the fourth most common cause of cancer death worldwide and its survival rate depends on the stage in which it is detected on hence the necessity for an early colon screening. There are several screening techniques but colonoscopy is still nowadays the gold standard, although it has some drawbacks such as the miss rate. Our contribution, in the field of intelligent systems for colonoscopy, aims at providing a polyp localization and a polyp segmentation system based on a model of appearance for polyps. To develop both methods we define a model of appearance for polyps, which describes a polyp as enclosed by intensity valleys. The novelty of our contribution resides on the fact that we include in our model aspects of the image formation and we also consider the presence of other elements from the endoluminal scene such as specular highlights and blood vessels, which have an impact on the performance of our methods. In order to develop our polyp localization method we accumulate valley information in order to generate energy maps, which are also used to guide the polyp segmentation. Our methods achieve promising results in polyp localization and segmentation. As we want to explore the usability of our methods we present a comparative analysis between physicians fixations obtained via an eye tracking device and our polyp localization method. The results show that our method is indistinguishable to novice physicians although it is far from expert physicians. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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F. Javier Sanchez;Fernando Vilariño |
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Call Number |
Admin @ si @ Ber2012 |
Serial |
2211 |
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