Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy

Vilariño, Fernando; Spyridonos, Panagiota; Pujol, Oriol; Vitrià, Jordi; Radeva, Petia; Iorio, Fosca De

doi:10.1109/icpr.2006.296

Cited by 59 publications

(33 citation statements)

References 8 publications

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“…The journey time is about eight hours and the stored data consists of approximately 50,000 frames. One of the most salient features of this technique relates to the lack of need of hospitalization or specialized stuff, overcoming most of the main drawbacks related to classical endoscopy [1]. On the other hand, the major weakness of the WCE is the requirement of significant diagnosing time (approximately 2 hours) and close concentration of an expert clinician, driving this clinical routine not feasible for certain clinical scenarios [2].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Informative Frames from Wireless Capsule Endoscopic Video Using Color and Texture Features

Bashar

Mori

Suenaga

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. Despite emerging technology, wireless capsule endoscopy needs high amount of diagnosis-time due to the presence of many useless frames, created by turbid fluids, foods, and faecal materials. These materials and fluids present a wide range of colors and/or bubble-like texture patterns. We, therefore, propose a cascade method for informative frame detection, which uses local color histogram to isolate highly contaminated non-bubbled (HCN) frames, and Gauss Laguerre Transform (GLT) based multiresolution norm-1 energy feature to isolate significantly bubbled (SB) frames. Supervised support vector machine is used to classify HCN frames (Stage-1), while automatic bubble segmentation followed by threshold operation(Stage-2) is adopted to detect informative frames by isolating SB frames. An experiment with 20,558 frames from the three videos shows 97.48 % average detection accuracy by the proposed method, when compared with methods adopting Gabor based-(75.52%) and discrete wavelet based features (63.15%) with the same color feature.

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Section: Introductionmentioning

confidence: 99%

“…Recently, Vilarino et al [1] applied Gabor based texture feature to segment intestinal juices from WCE frames. However, the effects of residual foods, and faecal materials remain unexplored except bubble-patterns in the intestinal juices.…”

Section: Introductionmentioning

confidence: 99%

Detecting Informative Frames from Wireless Capsule Endoscopic Video Using Color and Texture Features

Bashar

Mori

Suenaga

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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show abstract

“…In this effort, Gabor filters have been used to detect bubble like shape of intestinal juices [51], while color histograms together with a SVM classifier were being used to detect intestinal content [52]. A threestage cascade to detect informative frames has been proposed in [51].…”

Section: G Classifiers For Detection Of Non-informative Framesmentioning

confidence: 99%

Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

Prashanth¹,

Vani²

2017

IJIGSP

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Abstract-Wireless Capsule Endoscopy (WCE) is one of the methods for examination of gastrointestinal (GI) disorders such as obscure GI bleeding, Crohns disease, polyps etc. WCE has been recognized as a less expensive and painless procedure for the diagnosis of GI tract. This paper examines the various image classifiers designed and developed for the purpose of endoscopy focusing specifically on WCE. It is revealed that designing a suitable image classifier is an important prerequisite for accurate and precise diagnosis of malignancy in WCE. The assessment on various image classifiers used for the diagnosis of pathologies in different parts of GI tract shows that classifiers have reduced the diagnosis time for medical experts and also provided reasonably accurate diagnosis of malignancy. However, correlating classifiers and related pathologies is still observed to be challenging. In view of the fact that early detection may decrease the mortality rate significantly, inclination towards computer aided diagnosis are expected to increase in future. There is a need for advanced research in the development of a robust computer aided diagnosis system, capable of diagnosis of various pathologies in GI tract with higher degree of accuracy and reliability. Further, the study depicts that a direct comparison of results of classifier such as accuracy, prediction, sensitivity, specificity and precision to evaluate its performance is challenging due to diversity of image databases. More research is needed to identify and reduce the uncertainties in the application of image classifier to improve the diagnosis accuracy.

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“…Computational approaches coping with the analysis of the WCE video include the development of special annotation tools to auto-bookmark abnormalities [3]; classification approaches that perform tissue discrimination either between normal and abnormal regions [4] or between different organs [5][6][7]; synergistic methodologies such as image registration techniques and L-G graphs for the detection of abnormal patterns in WCE images [8]; clustering techniques for blood detection [9]; neural network techniques for classification or detection of abnormal patterns [10,16]; intestinal motility assessment methodologies [13,15]; and other approaches that aim either to image enhancement [12] or to the rejection of invalid parts of the WCE video by performing intestinal juice detection [14].…”

Section: Introductionmentioning

confidence: 99%

Automatic frame reduction of Wireless Capsule Endoscopy video

Tsevas

Iakovidis

Maroulis

et al. 2008

2008 8th IEEE International Conference on BioInformatics and BioEngineering

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Abstract-Wireless Capsule Endoscopy (WCE) is a noninvasive colour imaging technique that has been introduced for the screening of the gastrointestinal tract and especially the small intestine. WCE is performed by a wireless swallowable endoscopic capsule that transmits more than 50,000 video frames per examination. The visual inspection of the resulting video is a highly time-consuming task even for the experienced gastroenterologist. In this paper we propose a novel WCE video summarization approach which is subsequently evaluated using real world patient data. The proposed approach aims to the reduction of the number of the video frames to be visually inspected so as to enable significant reduction in the video assessment time. It is based on clustering using symmetric nonnegative matrix factorization initialized by the fuzzy c-means algorithm and supported by non-negative Lagrangian relaxation to extract a subset of video scenes containing the most representative frames from an entire examination. Real world patient data that display abnormal findings at several sites in the small intestine were annotated by expert gastroenterologists in order to experimentally evaluate the proposed approach. The results demonstrate that the suggested approach leads to significant reduction of the total number of frames in the input video without losing critical information related to the abnormal regions of the small intestine.

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Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy

Cited by 59 publications

References 8 publications

Detecting Informative Frames from Wireless Capsule Endoscopic Video Using Color and Texture Features

Detecting Informative Frames from Wireless Capsule Endoscopic Video Using Color and Texture Features

Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

Automatic frame reduction of Wireless Capsule Endoscopy video

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