Wireless Capsule Endoscopy Video Research Articles

Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software

The advent of wireless capsule endoscopy (WCE) has revolutionized the diagnostic approach to small-bowel disease. However, the task of reviewing WCE video sequences is laborious and time-consuming; software tools offering automated video analysis would enable a timelier and potentially a more accurate diagnosis. To assess the validity of innovative, automatic lesion-detection software in WCE. A color feature-based pattern recognition methodology was devised and applied to the aforementioned image group. This study was performed at the Royal Infirmary of Edinburgh, United Kingdom, and the Technological Educational Institute of Central Greece, Lamia, Greece. A total of 137 deidentified WCE single images, 77 showing pathology and 60 normal images. The proposed methodology, unlike state-of-the-art approaches, is capable of detecting several different types of lesions. The average performance, in terms of the area under the receiver-operating characteristic curve, reached 89.2 ± 0.9%. The best average performance was obtained for angiectasias (97.5 ± 2.4%) and nodular lymphangiectasias (96.3 ± 3.6%). Single expert for annotation of pathologies, single type of WCE model, use of single images instead of entire WCE videos. A simple, yet effective, approach allowing automatic detection of all types of abnormalities in capsule endoscopy is presented. Based on color pattern recognition, it outperforms previous state-of-the-art approaches. Moreover, it is robust in the presence of luminal contents and is capable of detecting even very small lesions.

Bayesian-based localization of wireless capsule endoscope using received signal strength.

In wireless body area sensor networking (WBASN) applications such as gastrointestinal (GI) tract monitoring using wireless video capsule endoscopy (WCE), the performance of out-of-body wireless link propagating through different body media (i.e. blood, fat, muscle and bone) is still under investigation. Most of the localization algorithms are vulnerable to the variations of path-loss coefficient resulting in unreliable location estimation. In this paper, we propose a novel robust probabilistic Bayesian-based approach using received-signal-strength (RSS) measurements that accounts for Rayleigh fading, variable path-loss exponent and uncertainty in location information received from the neighboring nodes and anchors. The results of this study showed that the localization root mean square error of our Bayesian-based method was 1.6 mm which was very close to the optimum Cramer-Rao lower bound (CRLB) and significantly smaller than that of other existing localization approaches (i.e. classical MDS (64.2mm), dwMDS (32.2mm), MLE (36.3mm) and POCS (2.3mm)).

Current role of endoscopy in inflammatory bowel disease diagnosis and management.

This review will summarize the role of endoscopy in the diagnosis of inflammatory bowel disease (IBD), in assessing its activity, its management, interventional endoscopy, and cancer surveillance. Endoscopy in IBD underwent major advances in the recent years, with the emergence of new techniques such as wireless video capsule endoscopy (WCE), device-assisted enteroscopy (DAE), chromoendoscopy, and confocal endomicroscopy. WCE is a minimally invasive tool, enabling the visualization of the entire small bowel mucosa. It has gained a substantial role in the evaluation of patients with suspected Crohn's disease and indeterminate colitis. With the correct use of the International Conference on Capsule Endoscopy criteria, WCE has a high positive predictive value in patients with suspected Crohn's disease. Moreover, WCE has a very high negative predictive value in patients with suspected Crohn's disease. DAE has established its role as a complementary tool in cases where there is need of biopsies or dilatation of strictures. Chromoendoscopy and confocal endomicroscopy are techniques that may assist in cancer surveillance in IBD patients. Endoscopy has a major role in the diagnosis of IBD, assessing its extent, treating some of its complications, assessing the success of various treatments, and as a predictor of disease course.

Automated Bleeding Detection in Capsule Endoscopy Videos Using Statistical Features and Region Growing

Wireless Capsule Endoscopy (WCE) is a technology in the field of endoscopic imaging which facilitates direct visualization of the entire small intestine. Many algorithms are being developed to automatically identify clinically important frames in WCE videos. This paper presents a supervised method for automated detection of bleeding regions present in WCE frames or images. The proposed method characterizes the image regions by using statistical features derived from the first order histogram probability of the three planes of RGB color space. Despite being inconsistent and tiresome, manual selection of regions has been a popular technique for creating training data in the studies of capsule endoscopic images. We propose a semi-automatic region-annotation algorithm for creating training data efficiently. All possible combinations of different features are exhaustively analyzed to find the optimum feature set with the best performance. During operation, regions from images are obtained by applying a segmentation method. Finally, a trained neural network recognizes the patterns of the data arising from bleeding and non-bleeding regions.

Computer-Aided Bleeding Detection in WCE Video

Wireless capsule endoscopy (WCE) can directly take digital images in the gastrointestinal tract of a patient. It has opened a new chapter in small intestine examination. However, a major problem associated with this technology is that too many images need to be manually examined by clinicians. Currently, there is no standard for capsule endoscopy image interpretation and classification. Most state-of-the-art CAD methods often suffer from poor performance, high computational cost, or multiple empirical thresholds. In this paper, a new method for rapid bleeding detection in the WCE video is proposed. We group pixels through superpixel segmentation to reduce the computational complexity while maintaining high diagnostic accuracy. Feature of each superpixel is extracted using the red ratio in RGB space and fed into support vector machine for classification. Also, the influence of edge pixels has been removed in this paper. Comparative experiments show that our algorithm is superior to the existing methods in terms of sensitivity, specificity, and accuracy.

Computerized 3-dimensional localization of a video capsule in the abdominal cavity: validation by digital radiography

Wireless video capsule endoscopy allows the noninvasive visualization of the small intestine. Currently, capsules do not provide localization information while traversing the GI tract. To report on the radiological validation of 3-dimensional localization software incorporated in a newly developed capsule. By using radiofrequency transmission, the software measures the strength of the capsule's signal to locate the position of the capsule. This study was performed at the University of Massachusetts Medical Center, Worcester, Mass. Thirty healthy volunteers consented to the experimental procedure. After ingestion of the capsule, subjects had 5 sets of anteroposterior and lateral radiographs taken every 30 minutes while the software calculated the position of the capsule. By using the radiographs, we calculated the location of the capsule in the abdominal cavity and compared the results with those generated by the software. Average error (and standard deviation) among the 3-dimensional coordinates was X, 2.00 cm (1.64); Y, 2.64 cm (2.39); and Z, 2.51 cm (1.83). The average total spatial error among all measurements was 13.26 cm(3) (22.72). There was a correlation between increased subject body mass index and the 3-dimensional software measurement error. This study was performed in healthy volunteers and needs further validation in patients with small intestinal disorders. The new 3-dimensional software provides localization of the capsule consistent with radiological observations. However, further validation of the software's clinical utility is required with a prospective clinical trial.

Hierarchical key frame extraction for wireless capsule endoscopy video based on the saliency map

Wireless capsule endoscopy (WCE) is a non-invasive imaging technique to examine the entire small intestine. This paper presents a novel hierarchical key frame extraction algorithm based on the saliency map to automatically select a small number of key informative frames in the WCE video to assist the diagnostic process. We start from calculating the saliency map and extracting the inside colour, texture and shape features. We then compute the image information entropies with adapted weights on three features; calculate the differences of entropies in the successive frames and extract the first level key frames to divide the video into multi-segments. Finally, the mean shift clustering method is applied on each segment to obtain the final key frames. The proposed technique achieves the fidelity of 95.07% and the compression ratio of 87.9% on average, validating that the proposed scheme is highly promising for the key frame extraction in the WCE video.

Informative and Uninformative Regions Detection in WCE Frames

Wireless capsule endoscopy (WCE) is relatively a new device which investigates the entire gastrointestinal (GI). About 55000 frames are captured during an examination (two frames per second). Thus, it is beneϐicial to ϐind an automatic method to detect diseases frames or regions of a frame. The WCE videos have lots of uninformative regions (such as intestinal juice, bubbled, and dark regions); therefore, preprocessing is useful and necessary in diseases detection. In this paper, three practical methods are introduced to detect the informative and uninformative regions in a frame. In order to achieve this goal, morphological operations, fuzzy k-means, sigmoid function, statistic features, Gabor ϐilters, Fisher test, neural network, and discriminators in the HSV color space are used to detect uninformative regions (do not carry clinical information) in a frame. Our experimental results indicate that precision, sensitivity, accuracy, and speciϐicity are respectively 97.76%, 97.80%, 98.15%, and 98.40% in the ϐirst method, 93.32%, 84.60%, 91.05%, and 95.67%, respectively in the second one, and 93.32%, 84.60%, 91.05%, and 95.67%, respectively in the third method.

Video-based measurements for wireless capsule endoscope tracking

The wireless capsule endoscope is a swallowable medical device equipped with a miniature camera enabling the visual examination of the gastrointestinal (GI) tract. It wirelessly transmits thousands of images to an external video recording system, while its location and orientation are being tracked approximately by external sensor arrays. In this paper we investigate a video-based approach to tracking the capsule endoscope without requiring any external equipment. The proposed method involves extraction of speeded up robust features from video frames, registration of consecutive frames based on the random sample consensus algorithm, and estimation of the displacement and rotation of interest points within these frames. The results obtained by the application of this method on wireless capsule endoscopy videos indicate its effectiveness and improved performance over the state of the art. The findings of this research pave the way for a cost-effective localization and travel distance measurement of capsule endoscopes in the GI tract, which could contribute in the planning of more accurate surgical interventions.

Management of preventing recurrent bleeding angiodysplasia of the intestinal tract with octreotide

Objective: We assessed the efficacy of octreotide in a cohort of 25 patients admitted in the Internal Medicine Department for angiodysplasia bleeding (AB). Material and methods: We report the treatment results of octreotide in 25 patients admitted for AB from January 2003 to December 2007. They had never received octreotide. After discharge, they were followed up in our consult to a maximum of five years. Octreotide was prescribed (100μg/12h). Patients were diagnosed of angiodysplasia if they had iron deficit anemia with positive fecal occult blood test or macroscopic bleeding and there were no any other possible source of bleeding in the intestinal tract after a complete work-up including endoscopy and wireless video capsule endoscopy.

Reducing redundancy in wireless capsule endoscopy videos

We eliminate similar frames from a wireless capsule endoscopy video of the human intestines to maximize spatial coverage and minimize the redundancy in images. We combine an intensity correction method with a method based an optical flow and features to detect and reduce near-duplicate images acquired during the repetitive backward and forward egomotions due to peristalsis. In experiments, this technique reduced duplicate image of 52.3% from images of the small intestine.

A Review of Machine-Vision-Based Analysis of Wireless Capsule Endoscopy Video

Wireless capsule endoscopy (WCE) enables a physician to diagnose a patient's digestive system without surgical procedures. However, it takes 1-2 hours for a gastroenterologist to examine the video. To speed up the review process, a number of analysis techniques based on machine vision have been proposed by computer science researchers. In order to train a machine to understand the semantics of an image, the image contents need to be translated into numerical form first. The numerical form of the image is known as image abstraction. The process of selecting relevant image features is often determined by the modality of medical images and the nature of the diagnoses. For example, there are radiographic projection-based images (e.g., X-rays and PET scans), tomography-based images (e.g., MRT and CT scans), and photography-based images (e.g., endoscopy, dermatology, and microscopic histology). Each modality imposes unique image-dependent restrictions for automatic and medically meaningful image abstraction processes. In this paper, we review the current development of machine-vision-based analysis of WCE video, focusing on the research that identifies specific gastrointestinal (GI) pathology and methods of shot boundary detection.

Adaptable image cuts for motility inspection using WCE

The Wireless Capsule Endoscopy (WCE) technology allows the visualization of the whole small intestine tract. Since the capsule is freely moving, mainly by the means of peristalsis, the data acquired during the study gives a lot of information about the intestinal motility. However, due to: (1) huge amount of frames, (2) complex intestinal scene appearance and (3) intestinal dynamics that make difficult the visualization of the small intestine physiological phenomena, the analysis of the WCE data requires computer-aided systems to speed up the analysis. In this paper, we propose an efficient algorithm for building a novel representation of the WCE video data, optimal for motility analysis and inspection. The algorithm transforms the 3D video data into 2D longitudinal view by choosing the most informative, from the intestinal motility point of view, part of each frame. This step maximizes the lumen visibility in its longitudinal extension. The task of finding “the best longitudinal view” has been defined as a cost function optimization problem which global minimum is obtained by using Dynamic Programming. Validation on both synthetic data and WCE data shows that the adaptive longitudinal view is a good alternative to the traditional motility analysis done by video analysis. The proposed novel data representation a new, holistic insight into the small intestine motility, allowing to easily define and analyze motility events that are difficult to spot by analyzing WCE video. Moreover, the visual inspection of small intestine motility is 4 times faster then by means of video skimming of the WCE.

RF Localization for Wireless Video Capsule Endoscopy

RF localization science and technology started with the global positioning systems for outdoor areas, and it then transformed into wireless indoor geolocation. The next step in the evolution of this science is the transfor- mation into RF localization inside the human body. The first major application for this technology is the localiza- tion of the wireless video capsule endoscope (VCE) that has been in the clinical arena for 12 years. While physi- cians can receive clear images of abnormalities in the gastrointestinal tract with VCE devices, they have little idea of their exact location inside the GI tract. To localize intestinal abnormalities, physicians routinely use radio- logical, endoscopic or surgical operations. If we could use the RF signal radiated from the capsule to also locate these devices, not only can physicians discover medical prob- lems, but they can also learn where the problems are located. However, finding a realistic RF localization solu- tion for the endoscopy capsule is a very challenging task, because the inside of the human body is a difficult envi- ronment for experimentation and visualization. In addition, we have no-idea how the capsule moves and rotates in its 3D journey in this non-homogeneous medium for radio propagation. In this paper, we describe how we can design a cyber physical system (CPS) for experimental testing and visualization of interior of the human body that can be used for solving the RF localization problem for the endoscopy capsule. We also address the scientific challenges that face and the appropriate technical approaches for solving this problem.

AUTOMATIC DETECTION OF SIMILARITIES AND DIFFERENCES BETWEEN SMALL BOWEL POLYPS AND ULCERS WITH A DATA MINING APPROACH IN WIRELESS CAPSULE ENDOSCOPY VIDEOS

Over the past decade Wireless Capsule Endoscopy (WCE) technology has become a very useful tool for diagnosing diseases within the human digestive tract. Using WCE physicians can examine the digestive tract in a minimum invasive way searching for pathological abnormalities such as bleeding, polyps, ulcers and Crohn's disease. In order for WCE to be more effective for gastroenterologists, engineers have developed software methods to automatically detect these diseases at high successful rate. Using proposed a synergistic methodology for automatic discovering polyps (protrusions) and ulcers in WCE video frames, a data mining approach is used that offers useful information about ulcers, polyps and normal tissues and their visual similarities. Finally, results of the methodology are given and statistical comparisons are also presented relevant to other works.

Bayesian Source Localization in Networks with Heterogeneous Transmission Medium

Precise positioning and tracking information in networks with a heterogeneous transmission medium presents a novel networking paradigm. Most of the localization algorithms are vulnerable to the variations of signal propagation speed, dielectric constant, and path-loss coefficient resulting in unreliable location estimation. In this paper, we propose a novel robust probabilistic Bayesian-based approach using received-signal-strength (RSS) measurements with varying path-loss exponent in wireless networks with heterogeneous medium. An application of such a localization method is relative positioning of nodes in a wireless network with a heterogeneous medium such as gastrointestinal tract monitoring using wireless video capsule endoscopy. The results of this study showed that the localization root mean square error (RMSE) of our Bayesian-based method when a sensor node was covered by four anchors was 1.0 mm which is smaller than that of other existing localization approaches under the same conditions such as classical MDS (43.1 mm), dwMDS (24.7 mm), MLE (21.8 mm) and POCS (1.7 mm). Copyright © 2012 Institute of Navigation

Wireless Capsule Endoscopy Video Reduction Based on Camera Motion Estimation

Wireless capsule endoscopy (WCE) is a novel technology aiming for investigating the diseases and abnormalities in small intestine. The major drawback of WCE examination is that it takes a long time to examine the whole WCE video. In this paper, we present a new reduction scheme for WCE video to reduce the examination time. To achieve this task, a WCE video motion model is proposed. Under this motion model, the WCE imaging motion is estimated in two stages (the coarse level and the fine level). In the coarse level, the WCE camera motion is estimated with a combination of Bee Algorithm and Mutual Information. In the fine level, the local gastrointestinal tract motion is estimated with SIFT flow. Based on the result of WCE imaging motion estimation, the reduction scheme preserves key images in WCE video with scene changes. From experimental results, we notice that the proposed motion model is suitable for the motion estimation in successive WCE images. Through the comparison with APRS and FCM-NMF scheme, our scheme can produce an acceptable reduction sequence for browsing and examination.

Bleeding Detection in Wireless Capsule Endoscopy Using Color Similarity Coefficient

Wireless Capsule Endoscopy (WCE) generates a large number of images in one examination of a patient. It is very laborious and time-consuming to detect the WCE video, and limits the wider application of WCE. It is urgent and necessary to develop an automatic and intelligent computer aided bleeding detection technique. This paper proposes the color vector similarity coefficients to measure the color similarity, and based on which, a novel algorithm is implemented to recognize the bleeding in WCE images. The novel algorithm is implemented in RGB color space, and is featured with simple computation and practicability. The experiments show the sensitivity and specificity of this algorithm are 90% and 97% respectively.

Evaluation of four time-saving methods of reading capsule endoscopy videos

Review of wireless capsule endoscopy recordings is time consuming. The aim of this study was to evaluate four time-saving methods offered with Rapid Software. A total of 100 wireless capsule endoscopy videos with abnormal findings were evaluated using five different ways of viewing: (a) manual mode at a speed of 10 frames per second (fps), (b) manual mode at a speed of 20 fps, (c) manual mode with a simultaneous display of two images at a speed of 20 fps, (d) automatic mode at a speed of 10 fps, and (e) quickview mode at a speed of 3 fps. Then, we calculated the concordance of abnormal findings between each one of the four time-saving methods using method A. The mean reading time with time-saving methods was significantly shorter than with method A (method A: 59.8 min, method B: 30 min, method C: 30.2 min, method D: 32.2 min, method E: 16.3 min). The agreement in finding abnormal lesions between method A and the four evaluated methods was excellent and almost perfect (κ>0.8), except for quickview in recognizing polyps. Diagnostic miss rate was 1% for method D, 2% for B and C, and 12% for E. No tumors and cases of celiac or Crohn's disease were lost by all four methods. We conclude that manual mode/20 fps, the simultaneous projection of two images/20 fps, and automatic mode/10 fps have minimal diagnostic miss rates and can safely replace slower modes in clinical practice. The quickview mode is a safe diagnostic tool only when larger or diffuse lesions are suspected, such as Crohn's or celiac disease.

Small bowel endoscopy and coeliac disease

Coeliac disease (CD) is a gluten-responsive, chronic inflammatory enteropathy that shares many features with classical autoimmune diseases. Coeliac disease affects about 1-2% of Caucasians, North Africans and Asians who possess the necessary susceptibility genes encoding HLA DQ2 or HLA DQ8. It is not only unique among the autoimmune diseases in that the precise trigger (gluten from wheat, rye and barley) has been identified, but also in that it has lent itself well to advancements in endoscopic imaging. Since its introduction, flexible endoscopy has allowed tissue to be collected from the small bowel with relative ease and safety, and recently has facilitated direct imaging and sampling of the entire small intestine. It is now fifty years since the Crosby capsule first allowed clinicians the ability to non-surgically biopsy the small bowel leading to an enhanced diagnosis of coeliac disease. The introduction of wireless video capsule endoscopy (VCE), small bowel enteroscopy and in particular double balloon enteroscopy (DBE), have expedited the accurate diagnosis of coeliac disease and its more serious complications such as small bowel adenocarcinoma, refractory coeliac disease type II (RCDII) and enteropathy associated T cell lymphoma (EATL).