| Records |
| Author |
Jorge Bernal |
| Title |
Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps |
Type |
Book Whole |
| Year |
2012 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
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. |
| Address |
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| Corporate Author |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
F. Javier Sanchez;Fernando Vilariño |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
800 |
Expedition |
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Conference |
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| Notes |
MV |
Approved |
no |
Call Number  |
Admin @ si @ Ber2012 |
Serial |
2211 |
| Permanent link to this record |
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| |
| Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
| Title |
Reduction of Pattern Search Area in Colonoscopy Images by Merging Non-Informative Regions |
Type |
Conference Article |
| Year |
2010 |
Publication |
28th Congreso Anual de la Sociedad Española de Ingeniería Biomédica |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| 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. |
| Address |
Madrid (Spain) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
800 |
Expedition |
|
Conference |
CASEIB |
| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ BSV2010 |
Serial |
1469 |
| Permanent link to this record |
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| |
| Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
| Title |
Towards Automatic Polyp Detection with a Polyp Appearance Model |
Type |
Journal Article |
| Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
45 |
Issue |
9 |
Pages |
3166-3182 |
| Keywords |
Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot |
| Abstract |
This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Elsevier |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0031-3203 |
ISBN |
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Medium |
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| Area |
800 |
Expedition |
|
Conference |
IbPRIA |
| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ BSV2012; IAM @ iam |
Serial |
1997 |
| Permanent link to this record |
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| |
| Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
| Title |
Impact of Image Preprocessing Methods on Polyp Localization in Colonoscopy Frames |
Type |
Conference Article |
| Year |
2013 |
Publication |
35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abbreviated Journal |
|
| Volume |
|
Issue |
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Pages |
7350 - 7354 |
| Keywords |
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| Abstract |
In this paper we present our image preprocessing methods as a key part of our automatic polyp localization scheme. These methods are used to assess the impact of different endoluminal scene elements when characterizing polyps. More precisely we tackle the influence of specular highlights, blood vessels and black mask surrounding the scene. Experimental results prove that the appropriate handling of these elements leads to a great improvement in polyp localization results. |
| Address |
Osaka; Japan; July 2013 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
|
| ISSN |
1557-170X |
ISBN |
|
Medium |
|
| Area |
800 |
Expedition |
|
Conference |
EMBC |
| Notes |
MV; 600.047; 600.060;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ BSV2013 |
Serial |
2286 |
| Permanent link to this record |
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| |
| Author |
Jorge Bernal; Fernando Vilariño; F. Javier Sanchez |
| Title |
Feature Detectors and Feature Descriptors: Where We Are Now |
Type |
Report |
| Year |
2010 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
| Volume |
154 |
Issue |
|
Pages |
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| Keywords |
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| 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. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
800 |
Expedition |
|
Conference |
|
| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ BVS2010; IAM @ iam @ BVS2010 |
Serial |
1348 |
| Permanent link to this record |
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| |
| Author |
Farhan Riaz; Fernando Vilariño; Mario Dinis-Ribeiro; Miguel Coimbraln |
| Title |
Identifying Potentially Cancerous Tissues in Chromoendoscopy Images |
Type |
Conference Article |
| Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
6669 |
Issue |
|
Pages |
709-716 |
| Keywords |
Endoscopy, Computer Assisted Diagnosis, Gradient. |
| 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. |
| Address |
Las Palmas de Gran Canaria. Spain |
| Corporate Author |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
Berlin |
Editor |
J. Vitria, J.M. Sanches, and M. Hernandez |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
|
Edition |
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| ISSN |
|
ISBN |
978-3-642-21256-7 |
Medium |
|
| Area |
800 |
Expedition |
|
Conference |
IbPRIA |
| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ RVD2011; IAM @ iam @ RVD2011 |
Serial |
1726 |
| Permanent link to this record |
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| |
| Author |
Carles Sanchez; Jorge Bernal; Debora Gil; F. Javier Sanchez |
| Title |
On-line lumen centre detection in gastrointestinal and respiratory endoscopy |
Type |
Conference Article |
| Year |
2013 |
Publication |
Second International Workshop Clinical Image-Based Procedures |
Abbreviated Journal |
|
| Volume |
8361 |
Issue |
|
Pages |
31-38 |
| Keywords |
Lumen centre detection; Bronchoscopy; Colonoscopy |
| Abstract |
We present in this paper a novel lumen centre detection for gastrointestinal and respiratory endoscopic images. The proposed method is based on the appearance and geometry of the lumen, which we defined as the darkest image region which centre is a hub of image gradients. Experimental results validated on the first public annotated gastro-respiratory database prove the reliability of the method for a wide range of images (with precision over 95 %). |
| Address |
Nagoya; Japan; September 2013 |
| Corporate Author |
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Thesis |
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| Publisher |
Springer International Publishing |
Place of Publication |
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Editor |
Erdt, Marius and Linguraru, Marius George and Oyarzun Laura, Cristina and Shekhar, Raj and Wesarg, Stefan and González Ballester, Miguel Angel and Drechsler, Klaus |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
978-3-319-05665-4 |
Medium |
|
| Area |
800 |
Expedition |
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Conference |
CLIP |
| Notes |
MV; IAM; 600.047; 600.044; 600.060 |
Approved |
no |
| Call Number |
Admin @ si @ SBG2013 |
Serial |
2302 |
| Permanent link to this record |
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| Author |
Santiago Segui; Michal Drozdzal; Fernando Vilariño; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
| Title |
Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy |
Type |
Journal Article |
| Year |
2012 |
Publication |
IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
| Volume |
16 |
Issue |
6 |
Pages |
1341-1352 |
| Keywords |
|
| 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. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1089-7771 |
ISBN |
|
Medium |
|
| Area |
800 |
Expedition |
|
Conference |
|
| Notes |
MILAB; MV; OR;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ SDV2012 |
Serial |
2124 |
| Permanent link to this record |
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| |
| Author |
Fernando Vilariño |
| Title |
A Machine Learning Approach for Intestinal Motility Assessment with Capsule Endoscopy |
Type |
Book Whole |
| Year |
2006 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
|
| Volume |
|
Issue |
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Pages |
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| Keywords |
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| 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 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. |
| Address |
CVC (UAB) |
| Corporate Author |
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Thesis |
Ph.D. thesis |
| Publisher |
|
Place of Publication |
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Editor |
Petia Radeva |
| Language |
|
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
84-933652-7-0 |
Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
800 |
Expedition |
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Conference |
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| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ Vil2006; IAM @ iam @ Vil2006 |
Serial |
738 |
| Permanent link to this record |
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| Author |
Santiago Segui; Laura Igual; Fernando Vilariño; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
| Title |
Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy |
Type |
Book Chapter |
| Year |
2008 |
Publication |
Computer Vision Systems. 6th International |
Abbreviated Journal |
|
| Volume |
5008 |
Issue |
|
Pages |
251–260 |
| Keywords |
|
| 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. |
| Address |
Santorini (Greece) |
| Corporate Author |
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Thesis |
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| Publisher |
Springer-Verlag |
Place of Publication |
Berlin Heidelberg |
Editor |
A. Gasteratos, M. Vincze, and J.K. Tsotsos |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-3-540-79546-9 |
Medium |
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| Area |
800 |
Expedition |
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Conference |
ICVS |
| Notes |
OR; MV; MILAB; SIAI |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ SIV2008; IAM @ iam @ SIV2008 |
Serial |
962 |
| Permanent link to this record |
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| Author |
Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
| Title |
Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions |
Type |
Book Chapter |
| Year |
2006 |
Publication |
9th International Conference on Medical Image Computing and Computer–Assisted Intervention |
Abbreviated Journal |
|
| Volume |
4191 |
Issue |
|
Pages |
161–168 |
| Keywords |
|
| Abstract |
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. |
| Address |
Copenhagen (Denmark) |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Verlag |
Place of Publication |
Berlin Heidelberg |
Editor |
R. Larsen, M. Nielsen, and J. Sporring |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
800 |
Expedition |
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Conference |
MICCAI06 |
| Notes |
MV;OR;MILAB;SIAI |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ SVV2006; IAM @ iam @ SVV2006 |
Serial |
725 |
| Permanent link to this record |
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| Author |
Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
| Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
Type |
Journal Article |
| Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
27 |
Issue |
8 |
Pages |
875–881 |
| Keywords |
ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
| Abstract |
Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
800 |
Expedition |
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Conference |
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| Notes |
MILAB;MV;SIAI |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
Serial |
647 |
| Permanent link to this record |
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| Author |
Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
| Title |
Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions |
Type |
Journal Article |
| Year |
2010 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
| Volume |
29 |
Issue |
2 |
Pages |
246-259 |
| Keywords |
|
| 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. |
| Address |
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| Corporate Author |
IEEE |
Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
|
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0278-0062 |
ISBN |
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Medium |
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| Area |
800 |
Expedition |
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Conference |
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| Notes |
MILAB;MV;OR;SIAI |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 |
Serial |
1281 |
| Permanent link to this record |
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| Author |
Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
| Title |
Cascade analysis for intestinal contraction detection |
Type |
Conference Article |
| Year |
2006 |
Publication |
20th International Congress and exhibition Computer Assisted Radiology and Surgery |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
9-10 |
| Keywords |
intestine video analysis, anisotropic features, support vector machine, cascade of classifiers |
| Abstract |
In this work, we address the study of intestinal contractions in a novel approach based on a machine learning framework to process data from Wireless Capsule Video Endoscopy. Wireless endoscopy represents a unique way to visualize the intestine motility by creating long videos to visualize intestine dynamics. In this paper we argue that to analyze huge amount of wireless endoscopy data and define robust methods for contraction detection we should base our approach on sophisticated machine learning techniques. In particular, we propose a cascade of classifiers in order to remove different physiological phenomenon and obtain the motility pattern of small intestines. Our results show obtaining high specificity and sensitivity rates that highlight the high efficiency of the selected approach and support the feasibility of the proposed methodology in the automatic detection and analysis of intestine contractions. |
| Address |
Osaka (Japan) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
800 |
Expedition |
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Conference |
CARS |
| Notes |
MV;OR;MILAB;SIAI |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ VSV2006a; IAM @ iam @ VSV2006h |
Serial |
726 |
| Permanent link to this record |
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| Author |
Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
| Title |
Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy |
Type |
Conference Article |
| Year |
2006 |
Publication |
18th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
4 |
Issue |
|
Pages |
719-722 |
| Keywords |
Clinical diagnosis , Endoscopes , Fluids and secretions , Gabor filters , Hospitals , Image sequence analysis , Intestines , Lighting , Shape , Visualization |
| Abstract |
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. |
| Address |
Hong Kong |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
0-7695-2521-0 |
Medium |
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| Area |
800 |
Expedition |
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Conference |
ICPR |
| Notes |
MV;OR;MILAB;SIAI |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ VSV2006b; IAM @ iam @ VSV2006g |
Serial |
727 |
| Permanent link to this record |
