Automated topographic segmentation and transit time estimation in endoscopic capsule exams

Robotic wireless capsule endoscopy: recent advances and upcoming technologies

Wireless capsule endoscopy (WCE) offers a non-invasive evaluation of the digestive system, eliminating the need for sedation and the risks associated with conventional endoscopic procedures. Its significance lies in diagnosing gastrointestinal tissue irregularities, especially in the small intestine. However, existing commercial WCE devices face limitations, such as the absence of autonomous lesion detection and treatment capabilities. Recent advancements in micro-electromechanical fabrication and computational methods have led to extensive research in sophisticated technology integration into commercial capsule endoscopes, intending to supersede wired endoscopes. This Review discusses the future requirements for intelligent capsule robots, providing a comparative evaluation of various methods' merits and disadvantages, and highlighting recent developments in six technologies relevant to WCE. These include near-field wireless power transmission, magnetic field active drive, ultra-wideband/intrabody communication, hybrid localization, AI-based autonomous lesion detection, and magnetic-controlled diagnosis and treatment. Moreover, we explore the feasibility for future "capsule surgeons".

Computer vision-based solutions to overcome the limitations of wireless capsule endoscopy

Endoscopic investigation plays a critical role in the diagnosis of gastrointestinal (GI) diseases. Since 2001, Wireless Capsule Endoscopy (WCE) has been available for small bowel exploration and is in continuous development. Over the last decade, WCE has achieved impressive improvements in areas such as miniaturisation, image quality and battery life. As a result, WCE is currently a very useful alternative to wired enteroscopy in the investigation of various small bowel abnormalities and has the potential to become the leading screening technique for the entire gastrointestinal tract. However, commercial solutions still have several limitations, namely incomplete examination and limited diagnostic capacity. These deficiencies are related to technical issues, such as image quality, motion estimation and power consumption management. Computational methods, based on image processing and analysis, can help to overcome these challenges and reduce both the time required by reviewers and human interpretation errors. Research groups have proposed a series of methods including algorithms for locating the capsule or lesion, assessing intestinal motility and improving image quality.In this work, we provide a critical review of computational vision-based methods for WCE image analysis aimed at overcoming the technological challenges of capsules. This article also reviews several representative public datasets used to evaluate the performance of WCE techniques and methods. Finally, some promising solutions of computational methods based on the analysis of multiple-camera endoscopic images are presented.

Classification of endoscopic image and video frames using distance metric-based learning with interpolated latent features

Conventional Endoscopy (CE) and Wireless Capsule Endoscopy (WCE) are well known tools for diagnosing gastrointestinal (GI) tract related disorders. Defining the anatomical location within the GI tract helps clinicians determine appropriate treatment options, which can reduce the need for repetitive endoscopy. Limited research addresses the localization of the anatomical location of WCE and CE images using classification, mainly due to the difficulty in collecting annotated data. In this study, we present a few-shot learning method based on distance metric learning which combines transfer-learning and manifold mixup schemes to localize and classify endoscopic images and video frames. The proposed method allows us to develop a pipeline for endoscopy video sequence localization that can be trained with only a few samples. The use of manifold mixup improves learning by increasing the number of training epochs while reducing overfitting and providing more accurate decision boundaries. A dataset is collected from 10 different anatomical positions of the human GI tract. Two models were trained using only 78 CE and 27 WCE annotated frames to predict the location of 25,700 and 1825 video frames from CE and WCE respectively. We performed subjective evaluation using nine gastroenterologists to validate the need of having such an automated system to localize endoscopic images and video frames. Our method achieved higher accuracy and a higher F1-score when compared with the scores from subjective evaluation. In addition, the results show improved performance with less cross-entropy loss when compared with several existing methods trained on the same datasets. This indicates that the proposed method has the potential to be used in endoscopy image classification.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11042-023-14982-1.

The Role of Capsule Endoscopy in Crohn's Disease: A Review

Crohn’s disease (CD) is a chronic inflammatory disorder with a predilection for the small bowel. Although awareness of this disorder has increased over the years, it remains a diagnostic challenge for many physicians. This is exacerbated by the rising incidence and high recurrence rate following therapy in certain individuals. It is currently agreed that a multimodality approach is the best one, but with the advent of new modalities, that could be changing. Furthermore, given its impact on the mental health of patients and the cost of treatment, it is pertinent that we arrive at not only convenient but accurate modalities in its diagnosis and management. Among these investigative modalities is the relatively novel capsule endoscopy (CE) that not only provides a more patient-friendly alternative but avoids the need for invasiveness. Asides from its diagnostic capability, its influence on therapy and monitoring of known CD patients following treatment has been shown. This article has reviewed the current literature comparing the relevance of CE with other available modalities in diagnosing CD patients. We explored its therapeutic impact and how it influences monitoring post-treatment in CD. This article also discusses the complications of CE and the possible solutions to these complications in the future.

Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges

Artificial intelligence (AI) has revolutionized the medical diagnostic process of various diseases. Since the manual reading of capsule endoscopy videos is a time-intensive, error-prone process, computerized algorithms have been introduced to automate this process. Over the past decade, the evolution of convolutional neural network (CNN) enabled AI to detect multiple lesions simultaneously with increasing accuracy and sensitivity. Difficulty in validating CNN performance and unique characteristics of capsule endoscopy images make computer-aided reading systems in capsule endoscopy still on a preclinical level. Although AI technology can be used as an auxiliary second observer in capsule endoscopy, it is expected that in the near future, it will effectively reduce the reading time and ultimately become an independent, integrated reading system.

Artificial intelligence and deep learning for small bowel capsule endoscopy

Capsule endoscopy is ideally suited to artificial intelligence-based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence-based clinical applications are likely to proliferate rapidly.

Colored Video Analysis in Wireless Capsule Endoscopy: A Survey of State-of-the-Art

Wireless Capsule Endoscopy (WCE) is a highly promising technology for gastrointestinal (GI) tract abnormality diagnosis. However, low image resolution and low frame rates are challenging issues in WCE. In addition, the relevant frames containing the features of interest for accurate diagnosis only constitute 1% of the complete video information. For these reasons, analyzing the WCE videos is still a time consuming and laborious examination for the gastroenterologists, which reduces WCE system usability. This leads to the emergent need to speed-up and automates the WCE video process for GI tract examinations. Consequently, the present work introduced the concept of WCE technology, including the structure of WCE systems, with a focus on the medical endoscopy video capturing process using image sensors. It discussed also the significant characteristics of the different GI tract for effective feature extraction. Furthermore, video approaches for bleeding and lesion detection in the WCE video were reported with computer-aided diagnosis systems in different applications to support the gastroenterologist in the WCE video analysis. In image enhancement, WCE video review time reduction is also discussed, while reporting the challenges and future perspectives, including the new trend to employ the deep learning models for feature Learning, polyp recognition, and classification, as a new opportunity for researchers to develop future WCE video analysis techniques.

Organic Boundary Location Based on Color-Texture of Visual Perception in Wireless Capsule Endoscopy Video

This paper addresses the problem of automatically locating the boundary between the stomach and the small intestine (the pylorus) in wireless capsule endoscopy (WCE) video. For efficient image segmentation, the color-saliency region detection (CSD) method is developed for obtaining the potentially valid region of the frame (VROF). To improve the accuracy of locating the pylorus, we design the Monitor-Judge model. On the one hand, the color-texture fusion feature of visual perception (CTVP) is constructed by grey level cooccurrence matrix (GLCM) feature from the maximum moments of the phase congruency covariance and hue-saturation histogram feature in HSI color space. On the other hand, support vector machine (SVM) classifier with the CTVP feature is utilized to locate the pylorus. The experimental results on 30 real WCE videos demonstrate that the proposed location method outperforms the related valuable techniques.

A novel method for automatically locating the pylorus in the wireless capsule endoscopy

Wireless capsule endoscopy (WCE) is a non-invasive technique used to examine the interiors of digestive tracts. Generally, the digestive tract can be divided into four segments: the entrance; stomach; small intestine; and large intestine. The stomach and the small intestine have a higher risk of infections than the other segments. In order to locate the diseased organ, an appropriate classification of the WCE images is necessary. In this article, a novel method is proposed for automatically locating the pylorus in WCE. The location of the pylorus is determined on two levels: rough-level and refined-level. In the rough-level, a short-term color change at the boundary between stomach and intestine can help us to find approximately 70–150 positions. In the refined-level, an improved Weber local descriptor (WLD) feature extraction method is designed for gray-scale images. Compared to the original WLD calculation method, the method for calculating the differential excitation is improved to give a higher level of robustness. A K-nearest neighbor (KNN) classifier is incorporated to segment these images around the approximate position into different regions. The proposed algorithm locates three most probable positions of the pylorus that were marked by the clinician. The experimental results indicate that the proposed method is effective.

Overview of technical solutions and assessment of clinical usefulness of capsule endoscopy

The paper presents an overview of endoscopic capsules with particular emphasis on technical aspects. It indicates common problems in capsule endoscopy such as: (1) limited wireless communication (2) the use of capsule endoscopy in the case of partial patency of the gastrointestinal tract, (3) limited imaging area, (4) external capsule control limitations. It also presents the prospects of capsule endoscopy, the most recent technical solutions for biopsy and the mobility of the capsule in the gastrointestinal tract. The paper shows the possibilities of increasing clinical usefulness of capsule endoscopy resulting from technological limitations. Attention has also been paid to the current role of capsule endoscopy in screening tests and the limitations of its effectiveness. The paper includes the author's recommendations concerning the direction of further research and the possibility of enhancing the scope of capsule endoscopy.

Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos

BACKGROUND AND OBJECTIVE

Wireless Capsule Endoscopy (WCE) can image the portions of the human gastrointestinal tract that were previously unreachable for conventional endoscopy examinations. A major drawback of this technology is that a large volume of data are to be analyzed in order to detect a disease which can be time-consuming and burdensome for the clinicians. Consequently, there is a dire need of computer-aided disease detection schemes to assist the clinicians. In this paper, we propose a real-time, computationally efficient and effective computerized bleeding detection technique applicable for WCE technology.

METHODS

The development of our proposed technique is based on the observation that characteristic patterns appear in the frequency spectrum of the WCE frames due to the presence of bleeding region. Discovering these discriminating patterns, we develop a texture-feature-descriptor-based-algorithm that operates on the Normalized Gray Level Co-occurrence Matrix (NGLCM) of the magnitude spectrum of the images. A new local texture descriptor called difference average that operates on NGLCM is also proposed. We also perform statistical validation of the proposed scheme.

RESULTS

The proposed algorithm was evaluated using a publicly available WCE database. The training set consisted of 600 bleeding and 600 non-bleeding frames. This set was used to train the SVM classifier. On the other hand, 860 bleeding and 860 non-bleeding images were selected from the rest of the extracted images to form the test set. The accuracy, sensitivity and specificity obtained from our method are 99.19%, 99.41% and 98.95% respectively which are significantly higher than state-of-the-art methods. In addition, the low computational cost of our method makes it suitable for real-time implementation.

CONCLUSION

This work proposes a bleeding detection algorithm that employs textural features from the magnitude spectrum of the WCE images. Experimental outcomes backed by statistical validations prove that the proposed algorithm is superior to the existing ones in terms of accuracy, sensitivity, specificity and computational cost.

Overview of technical solutions and assessment of clinical usefulness of capsule endoscopy

The paper presents an overview of endoscopic capsules with particular emphasis on technical aspects. It indicates common problems in capsule endoscopy such as: (1) limited wireless communication (2) the use of capsule endoscopy in the case of partial patency of the gastrointestinal tract, (3) limited imaging area, (4) external capsule control limitations. It also presents the prospects of capsule endoscopy, the most recent technical solutions for biopsy and the mobility of the capsule in the gastrointestinal tract. The paper shows the possibilities of increasing clinical usefulness of capsule endoscopy resulting from technological limitations. Attention has also been paid to the current role of capsule endoscopy in screening tests and the limitations of its effectiveness. The paper includes the author's recommendations concerning the direction of further research and the possibility of enhancing the scope of capsule endoscopy.

Quantitative measurements in capsule endoscopy

This review summarizes several approaches for quantitative measurement in capsule endoscopy. Video capsule endoscopy (VCE) typically provides wireless imaging of small bowel. Currently, a variety of quantitative measurements are implemented in commercially available hardware/software. The majority is proprietary and hence undisclosed algorithms. Measurement of amount of luminal contamination allows calculating scores from whole VCE studies. Other scores express the severity of small bowel lesions in Crohn׳s disease or the degree of villous atrophy in celiac disease. Image processing with numerous algorithms of textural and color feature extraction is further in the research focuses for automated image analysis. These tools aim to select single images with relevant lesions as blood, ulcers, polyps and tumors or to omit images showing only luminal contamination. Analysis of motility pattern, size measurement and determination of capsule localization are additional topics. Non-visual wireless capsules transmitting data acquired with specific sensors from the gastrointestinal (GI) tract are available for clinical routine. This includes pH measurement in the esophagus for the diagnosis of acid gastro-esophageal reflux. A wireless motility capsule provides GI motility analysis on the basis of pH, pressure, and temperature measurement. Electromagnetically tracking of another motility capsule allows visualization of motility. However, measurement of substances by GI capsules is of great interest but still at an early stage of development.

Exploiting gastrointestinal anatomy for organ classification in capsule endoscopy using locality preserving projections

Capsule Endoscopy is a technique designed to wirelessly image the small intestine within the gastrointestinal (GI) tract. Its main drawback is the vast amount of images it generates per patient, necessitating long screening sessions by the clinician. Previous studies have proposed to partially facilitate this process by automatically segmenting the GI tract into its constituent organs, thus identifying the region of interest. In this work, we propose to exploit the anatomical structure of the GI tract when carrying out dimensionality reduction on visual feature vectors that describe the capsule images. To this end, we suggest a novel adaptation of a technique called Locality Preserving Projections, and results show that this achieves an improved performance in organ classification and segmentation, at no additional computational or memory cost.

Comparative assessment of feature extraction methods for visual odometry in wireless capsule endoscopy

Wireless capsule endoscopy (WCE) enables the non-invasive examination of the gastrointestinal (GI) tract by a swallowable device equipped with a miniature camera. Accurate localization of the capsule in the GI tract enables accurate localization of abnormalities for medical interventions such as biopsy and polyp resection; therefore, the optimization of the localization outcome is important. Current approaches to endoscopic capsule localization are mainly based on external sensors and transit time estimations. Recently, we demonstrated the feasibility of capsule localization based-entirely-on visual features, without the use of external sensors. This technique relies on a motion estimation algorithm that enables measurements of the distance and the rotation of the capsule from the acquired video frames. Towards the determination of an optimal visual feature extraction technique for capsule motion estimation, an extensive comparative assessment of several state-of-the-art techniques, using a publicly available dataset, is presented. The results show that the minimization of the localization error is possible at the cost of computational efficiency. A localization error of approximately one order of magnitude higher than the minimal one can be considered as compromise for the use of current computationally efficient feature extraction techniques.

Software for enhanced video capsule endoscopy: challenges for essential progress

Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bowel diseases. Furthermore, VCE has the potential to become the leading screening technique for the entire gastrointestinal tract. Computational methods that can be implemented in software can enhance the diagnostic yield of VCE both in terms of efficiency and diagnostic accuracy. Since the appearance of the first capsule endoscope in clinical practice in 2001, information technology (IT) research groups have proposed a variety of such methods, including algorithms for detecting haemorrhage and lesions, reducing the reviewing time, localizing the capsule or lesion, assessing intestinal motility, enhancing the video quality and managing the data. Even though research is prolific (as measured by publication activity), the progress made during the past 5 years can only be considered as marginal with respect to clinically significant outcomes. One thing is clear-parallel pathways of medical and IT scientists exist, each publishing in their own area, but where do these research pathways meet? Could the proposed IT plans have any clinical effect and do clinicians really understand the limitations of VCE software? In this Review, we present an in-depth critical analysis that aims to inspire and align the agendas of the two scientific groups.

Granular computing in mosaicing of images from capsule endoscopy

This article introduces methods for modeling compound granules used in algorithms which could successfully construct a mosaic from the images coming from an endoscope capsule. In order to apply the algorithm, combined images must have a common area where the correspondence of points is determined. That allows to determine the transformation parameters to compensate movement of the capsule that occurs between moments when the mosaic images were acquired. The developed algorithm for images from the capsule endoscopy has proved to be faster and comparably accurate as commercial GDB-ICP algorithm.

Use of shape-from-shading to estimate three-dimensional architecture in the small intestinal lumen of celiac and control patients

BACKGROUND

As measured from videocapsule endoscopy images, the small intestinal mucosa of untreated celiac patients has significantly greater and more varied texture compared to normal patients. Three-dimensional modeling using shape-from-shading principles may further increase classification accuracy.

METHODS

A sequence of 200 consecutive videocapsule images acquired at a 2s(-1) frame rate and 576×576 pixel dimension, were obtained at four locations in the small intestinal lumen of ten patients with biopsy-proven celiac disease and ten control patients. Each two-dimensional image was converted to a three-dimensional architectural approximation by considering the 256 grayscale level to be linearly representative of image depth. From the resulting three-dimensional architecture, distinct luminal protrusions, representative of the macro-architecture, were automatically identified by computer algorithm. The range and number of protrusions per image, and their width and height, were determined for celiacs versus controls and tabulated as mean±SD.

RESULTS

The mean number of villous protrusions per image was 402.2±15.0 in celiacs versus 420.8±24.0 in controls (p<0.001). The average protrusion width was 14.7 pixels in celiacs versus 13.9 pixels in controls (p=0.01). The mean protrusion height was 3.10±2.34 grayscale levels for celiacs versus 2.70±0.43 grayscale levels for controls (p<0.001). Thus celiac patients had significantly fewer protrusions on the luminal surface of the small intestine as compared with controls, and these protrusions had greater dimensions, suggesting they are indicative of a mosaic (cobblestone) macro-architectural pattern which is common in celiacs.

CONCLUSIONS

Shape-from-shading modeling is useful to explore luminal macro-architecture and to detect significant differences in luminal morphology in celiac versus normal patients, which can increase the usefulness of videocapsule studies.

Impact of visual features on the segmentation of gastroenterology images using normalized cuts

Gastroenterology imaging is an essential tool to detect gastrointestinal cancer in patients. Computer-assisted diagnosis is desirable to help us improve the reliability of this detection. However, traditional computer vision methodologies, mainly segmentation, do not translate well to the specific visual characteristics of a gastroenterology imaging scenario. In this paper, we propose a novel method for the segmentation of gastroenterology images from two distinct imaging modalities and organs: chromoendoscopy (CH) and narrow-band imaging (NBI) from stomach and esophagus, respectively. We have used various visual features individually and their combinations (edgemaps, creaseness, and color) in normalized cuts image segmentation framework to segment ground truth datasets of 142 CH and 224 NBI images. Experiments show that an integration of edgemaps and creaseness in normalized cuts gives the best segmentation performance resulting in high-quality segmentations of the gastroenterology images.

Categorization and segmentation of intestinal content frames for wireless capsule endoscopy

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.

Towards integrating temporal information in capsule endoscopy image analysis

Analysis of Wireless Capsule Endoscopy (CE) images has become a very active area of research since this novel technology enabled access to previously inaccessible areas of the gastrointestinal tract, especially the small intestine. Art has investigated automatic segmentation of organ boundaries, detection of lesions and bleeding as well as other supervised and unsupervised analysis. However, all of this art has focused on treating the images as individual and independent observations that contribute towards a unique and separate decision. Given the overlap between the images, this is clearly not the case. A human, by contrast, performs assessment by combining the information seen in all neighboring views of the anatomy in a study. This article makes two significant contributions. Towards combining information from multiple images, we propose a supervised classification approach using an HMM framework. Secondly, we use a weak (k-NN) classifier to prototype and evaluate such a framework for regions of the GI tract containing polyps. The combined framework significantly improves the performance of the individual classifier and experiments show promising performance with accuracy > 0.9.

Capsule endoscopy: from current achievements to open challenges

Wireless capsule endoscopy (WCE) can be considered an example of disruptive technology since it represents an appealing alternative to traditional diagnostic techniques. This technology enables inspection of the digestive system without discomfort or need for sedation, thus preventing the risks of conventional endoscopy, and has the potential of encouraging patients to undergo gastrointestinal (GI) tract examinations. However, currently available clinical products are passive devices whose locomotion is driven by natural peristalsis, with the drawback of failing to capture the images of important GI tract regions, since the doctor is unable to control the capsule's motion and orientation. To address these limitations, many research groups are working to develop active locomotion devices that allow capsule endoscopy to be performed in a totally controlled manner. This would enable the doctor to steer the capsule towards interesting pathological areas and to accomplish medical tasks. This review presents a research update on WCE and describes the state of the art of the basic modules of current swallowable devices, together with a perspective on WCE potential for screening, diagnostic, and therapeutic endoscopic procedures.

Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images

Background

Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity.

Method

The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis.

Results

The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Wireless capsule endoscopy video segmentation using an unsupervised learning approach based on probabilistic latent semantic analysis with scale invariant features

Since wireless capsule endoscopy (WCE) is a novel technology for recording the videos of the digestive tract of a patient, the problem of segmenting the WCE video of the digestive tract into subvideos corresponding to the entrance, stomach, small intestine, and large intestine regions is not well addressed in the literature. A selected few papers addressing this problem follow supervised leaning approaches that presume availability of a large database of correctly labeled training samples. Considering the difficulties in procuring sizable WCE training data sets needed for achieving high classification accuracy, we introduce in this paper an unsupervised learning approach that employs Scale Invariant Feature Transform (SIFT) for extraction of local image features and the probabilistic latent semantic analysis (pLSA) model used in the linguistic content analysis for data clustering. Results of experimentation indicate that this method compares well in classification accuracy with the state-of-the-art supervised classification approaches to WCE video segmentation.

A wideband spiral antenna for ingestible capsule endoscope systems: experimental results in a human phantom and a pig

This paper presents the design of a wideband spiral antenna for ingestible capsule endoscope systems and a comparison between the experimental results in a human phantom and a pig under general anesthesia. As wireless capsule endoscope systems transmit real-time internal biological image data at a high resolution to external receivers and because they operate in the human body, a small wideband antenna is required. To incorporate these properties, a thick-arm spiral structure is applied to the designed antenna. To make practical and efficient use of antennas inside the human body, which is composed of a high dielectric and lossy material, the resonance characteristics and radiation patterns were evaluated through a measurement setup using a liquid human phantom. The total height of the designed antenna is 5 mm and the diameter is 10 mm. The fractional bandwidth of the fabricated antenna is about 21% with a voltage standing-wave ratio of less than 2, and it has an isotropic radiation pattern. These characteristics are suitable for wideband capsule endoscope systems. Moreover, the received power level was measured using the proposed antenna, a circular polarized receiver antenna, and a pig under general anesthesia. Finally, endoscopic capsule images in the stomach and large intestine were captured using an on-off keying transceiver system.

Automatic region-of-interest segmentation and pathology detection in magnetically guided capsule endoscopy

Magnetically-guided capsule endoscopy (MGCE) was introduced in 2010 as a procedure where a capsule in the stomach is navigated via an external magnetic field. The quality of the examination depends on the operator's ability to detect aspects of interest in real time. We present a novel two step computer-assisted diagnostic-procedure (CADP) algorithm for indicating gastritis and gastrointestinal bleedings in the stomach during the examination. First, we identify and exclude subregions of bubbles which can interfere with further processing. Then we address the challenge of lesion localization in an environment with changing contrast and lighting conditions. After a contrast-normalized filtering, feature extraction is performed. The proposed algorithm was tested on 300 images of different patients with uniformly distributed occurrences of the target pathologies. We correctly segmented 84.72% of bubble areas. A mean detection rate of 86% for the target pathologies was achieved during a 5-fold leave-one-out cross-validation.

Variational image segmentation for endoscopic human colonic aberrant crypt foci

The aim of this paper is to introduce a variational image segmentation method for assessing the aberrant crypt foci (ACF) in the human colon captured in vivo by endoscopy. ACF are thought to be precursors for colorectal cancer, and therefore their early detection may play an important clinical role. We enhance the active contours without edges model of Chan and Vese to account for the ACF's particular structure. We employ level sets to represent the segmentation boundaries and discretize in space by finite elements and in (artificial) time by finite differences. The approach is able to identify the ACF, their boundaries, and some of the internal crypts' orifices.

Intestinal motility assessment with video capsule endoscopy: automatic annotation of phasic intestinal contractions

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.

Computer-aided detection of bleeding regions for capsule endoscopy images

Capsule endoscopy (CE) has been widely used to diagnose diseases in human digestive tract. However, a tough problem of this new technology is that too many images to be inspected by eyes cause a huge burden to physicians, so it is significant to investigate computerized diagnosis methods. In this paper, a new computer-aided system aimed for bleeding region detection in CE images is proposed. This new system exploits color texture feature, an important clue used by physicians, to analyze status of gastrointestinal tract. We put forward a new idea of chrominance moment as the color part of color texture feature, which makes full use of Tchebichef polynomials and illumination invariant of hue/saturation/intensity color space. Combined with uniform local binary pattern, a current texture representation model, it can be applied to discriminate normal regions and bleeding regions in CE images. Classification of bleeding regions using multilayer perceptron neural network is then deployed to verify performance of the proposed color texture features. Experimental results on our bleeding image data show that the proposed scheme is promising in detecting bleeding regions.

Wireless capsule endoscopy color video segmentation

This paper describes the use of color image analysis to automatically discriminate between oesophagus, stomach, small intestine, and colon tissue in wireless capsule endoscopy (WCE). WCE uses "pill-cam" technology to recover color video imagery from the entire gastrointestinal tract. Accurately reviewing and reporting this data is a vital part of the examination, but it is tedious and time consuming. Automatic image analysis tools play an important role in supporting the clinician and speeding up this process. Our approach first divides the WCE image into subimages and rejects all subimages in which tissue is not clearly visible. We then create a feature vector combining color, texture, and motion information of the entire image and valid subimages. Color features are derived from hue saturation histograms, compressed using a hybrid transform, incorporating the discrete cosine transform and principal component analysis. A second feature combining color and texture information is derived using local binary patterns. The video is segmented into meaningful parts using support vector or multivariate Gaussian classifiers built within the framework of a hidden Markov model. We present experimental results that demonstrate the effectiveness of this method.