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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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Journal Article |
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Year |
2014 |
Publication |
Electronic Letters on Computer Vision and Image Analysis |
Abbreviated Journal |
ELCVIA |
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Volume |
13 |
Issue |
2 |
Pages |
9-10 |
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Keywords |
Colonoscopy; polyp localization; polyp segmentation; Eye-tracking |
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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. |
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Alicia Fornes; Volkmar Frinken |
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Notes |
MV |
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no |
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Call Number |
Admin @ si @ Ber2014 |
Serial |
2487 |
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Author |
Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol |
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Title |
Online Error-Correcting Output Codes |
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Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
32 |
Issue |
3 |
Pages |
458-467 |
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Abstract |
IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier. |
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Elsevier |
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North Holland |
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0167-8655 |
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Notes |
MILAB;OR;HuPBA;MV |
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Admin @ si @ EMP2011 |
Serial |
1714 |
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Author |
Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
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Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
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Journal Article |
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Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
27 |
Issue |
8 |
Pages |
875–881 |
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Keywords |
ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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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. |
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800 |
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MILAB;MV;SIAI |
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no |
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BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
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647 |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title |
Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions |
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Journal Article |
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Year |
2010 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
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Volume |
29 |
Issue |
2 |
Pages |
246-259 |
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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 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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IEEE |
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ISSN |
0278-0062 |
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800 |
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Notes |
MILAB;MV;OR;SIAI |
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no |
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Call Number |
BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 |
Serial |
1281 |
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Author |
Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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Title |
Motility bar: a new tool for motility analysis of endoluminal videos |
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Journal Article |
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Year |
2015 |
Publication |
Computers in Biology and Medicine |
Abbreviated Journal |
CBM |
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Volume |
65 |
Issue |
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Pages |
320-330 |
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Keywords |
Small intestine; Motility; WCE; Computer vision; Image classification |
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Abstract |
Wireless Capsule Endoscopy (WCE) provides a new perspective of the small intestine, since it enables, for the first time, visualization of the entire organ. However, the long visual video analysis time, due to the large number of data in a single WCE study, was an important factor impeding the widespread use of the capsule as a tool for intestinal abnormalities detection. Therefore, the introduction of WCE triggered a new field for the application of computational methods, and in particular, of computer vision. In this paper, we follow the computational approach and come up with a new perspective on the small intestine motility problem. Our approach consists of three steps: first, we review a tool for the visualization of the motility information contained in WCE video; second, we propose algorithms for the characterization of two motility building-blocks: contraction detector and lumen size estimation; finally, we introduce an approach to detect segments of stable motility behavior. Our claims are supported by an evaluation performed with 10 WCE videos, suggesting that our methods ably capture the intestinal motility information. |
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MILAB;MV |
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Call Number |
Admin @ si @ DSR2015 |
Serial |
2635 |
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