Localization Of Wireless Capsule Endoscope Research Articles

Ultra-Wideband Wireless Capsule Endoscope Localization Based on a Hybrid One-Shot Learning and Trilateration Method

Ultra-Wideband Wireless Capsule Endoscope Localization Based on a Hybrid One-Shot Learning and Trilateration Method

Wireless Capsule Endoscope Localization Using LSTM Network

Wireless Capsule Endoscope Localization Using LSTM Network

Calibrated analytical model for magnetic localization of wireless capsule endoscope based on onboard sensing

AbstractWireless capsule endoscopes (WCEs) are pill-sized camera-embedded devices that can provide visualization of the gastrointestinal (GI) tract by capturing and transmitting images to an external receiver. Determination of the exact location of the WCE is crucial for the accurate navigation of the WCE through external guidance, tracking of the GI abnormality, and the treatment of the detected disease. Despite the enormous progress in the real-time tracking of the WCE, a well-calibrated analytical model is still missing for the accurate localization of WCEs by the measurements from different onboard sensing units. In this paper, a well-calibrated analytical model for the magnetic localization of the WCE was established by optimizing the magnetic moment in the magnetic dipole model. The Jacobian-based iterative method was employed to solve the position of the WCE. An error model was established and experimentally verified for the analysis and prediction of the localization errors caused by inaccurate measurements from the magnetic field sensor. The assessment of the real-time localization of the WCE was performed via experimental trials using an external permanent magnet (EPM) mounted on a robotic manipulator and a WCE equipped with a 3-axis magnetic field sensor and an inertial measurement unit (IMU). The localization errors were measured under different translational and rotational motion modes and working spaces. The results showed that the selection of workspace (distance relative to the EPM) could lead to different positioning errors. The proposed magnetic localization method holds great potential for the real-time localization of WCEs when performing complex motions during GI diagnosis.

Path Loss Based Wireless Capsule Endoscope Localization Using Machine Learning Regression

Path Loss Based Wireless Capsule Endoscope Localization Using Machine Learning Regression

Magnetic Localization Method of Capsule Endoscope Based on Hybrid Model

Magnetic localization is an effective method for obtaining the pose of a wireless capsule endoscope (WCE) because it is able to provide the position and orientation of the magnet in places where visual methods are not available. In magnetic positioning, the magnetic dipole model is often used to calculate the magnetic field of a magnetic source. However, the magnetic field calculated by the magnetic dipole model introduces positioning errors when a permanent magnet is used as the magnetic source and the size of the permanent magnet cannot be ignored. In this paper, a WCE localization method was proposed based on the combination of the magnetic dipole model and the analytical model. A cylindrical magnet was mounted in the WCE. The magnetic field of the magnet was calculated using the magnetic dipole model and the rough position of the magnet was estimated using the Particle Swarm Optimization (PSO) algorithm; then the magnetic field of the magnet was described using the analytical model and the estimated result of the PSO algorithm was used as the initial value of the Levenberg-Marquardt (L-M) algorithm to calculate the pose of the WCE. Experiments on static and dynamic localization of magnet were carried out in the localization system. The experimental results verified the feasibility of the proposed method.

Wireless Capsule Endoscope Localization with Phase Detection Algorithm and Adaptive Body Model.

Wireless capsule endoscopes take and send photos of the human digestive tract, which are used for medical diagnosis. The capsule’s location enables exact identification of the regions with lesions. This can be carried out by analyzing the parameters of the electromagnetic wave received from the capsule. Because the human body is a complex heterogeneous environment that impacts the propagation of wireless signals, determining the distance between the transmitter and the receiver based on the received power level is challenging. An enhanced approach of identifying the location of endoscope capsules using a wireless signal phase detection algorithm is presented in this paper. For each capsule position, this technique uses adaptive estimation of human body model permittivity. This approach was tested using computer simulations in Remcom XFdtd software using a numerical, heterogeneous human body model, as well as measurements with physical phantom. The type of transmitting antenna employed in the capsule also has a significant impact on the suggested localization method’s accuracy. As a result, the helical antenna, which is smaller than the dipole, was chosen as the signal’s source. For both the numerical and physical phantom studies, the proposed technique with adaptive body model enhances localization accuracy by roughly 30%.

An Improved Method for Localization of Wireless Capsule Endoscope Using Direct Position Determination

A novel methodology for localization of wireless-capsule-endoscope (WCE) by using the direct-position-determination (DPD) method is proposed. A WCE enables the diagnose of gastrointestinal tract disorders in a non-invasive visual manner. Conventional methods proposed for WCE localization are not optimal in terms of positioning accuracy since they include two disjoint stages: i) estimation of initial parameters such as direction, time, or time-difference of arrival (DOA, TOA, TDOA), and ii) localization performed by intersecting the loci provided by initial parameters. Moreover, most of these methods can localize only one signal transmitter. In contrast, the considered DPD is a single-step method which processes data from all sensor elements simultaneously and can resolve several (theoretically one less than sensors number) co-channel signal sources in a medical ward. More precisely, the proposed method can concurrently localize a WCE and some beacons attached to the patient’s body, which can supersede the use of micro-electromechanical-systems (MEMS). In addition, the considered DPD can locate some WCEs for multiple patients at the same time. To do this, DPD seeks for the peaks of a spatial profit function (SPF) which is based on the statistical cumulants of the signals observed at the array sensors placed around the medical ward. Here, we propose the classical DPD (CDPD) as a coarse but fast, and the generalized DPD (GDPD) as a fine and high resolution method for an improved localization of WCE. Also, the proposed technique can be easily extended to any other indoor applications, where the dimensions of the whole monitoring area and receiving array are about some carrier wavelengths. The superiority of CDPD over DOA-based WCE localization technique, and GDPD over CDPD is verified through comprehensive numerical analysis. We have also drawn the Cramer-Rao lower bound (CRLB) as a benchmark for performance analysis of GDPD. Moreover, we introduced a tighter lower bound on the localization mean square error (MSE) achieved with multiple group arrays (MGLB), corresponding to CDPD and DOA-based methods, to show the superiority of GDPD over these techniques.

Wireless Capsule Endoscope Localization with Phase Detection Algorithm and Simplified Human Body Model

Wireless Capsule Endoscope Localization with Phase Detection Algorithm and Simplified Human Body Model

Hybrid multiscale affine and elastic image registration approach towards wireless capsule endoscope localization

Abstract Wireless capsule endoscope (WCE) enables the visualization of the interior of the gastrointestinal (GI) tract. In particular it is very important for the examination of regions in the small bowel that cannot be reached by conventional endoscopy techniques. However, when an abnormality is found in WCE images of the small bowel, it is unknown how far is this abnormality from an anatomical reference point. The primary objective of the present paper is to give a contribution to WCE localization, using image-based methods. The main focus of this work is on the description of a hybrid multiscale affine and elastic image registration approach, its experimental application on WCE videos, and comparison with a multiscale affine registration. The proposed approach intends to track the WCE motion, by using the successive WCE frames that image the walls of the elastic small intestine. It includes registrations that capture both rigid-like and non-rigid deformations, due respectively to the rigid-like WCE movement and the elastic deformation of the small intestine originated by the GI peristaltic movement. Furthermore, the proposed approach enables the extraction of two parameters (scale and rotation) from which the relative displacement and orientation of the WCE inside the GI tract can be derived, via projective geometry. Under this approach an indicator of the WCE speed can be inferred, which can be clinically useful for video interpretation. The results of the experimental tests with real WCE video frames show the good performance of the proposed approach, when elastic deformations of the small intestine are involved in successive frames, and its superiority with respect to a multiscale affine image registration, which accounts for rigid-like deformations only and discards elastic deformations.

Localization of Wireless Capsule Endoscope: A Systematic Review

Wireless capsule endoscope (WCE) is a notable invention introduced in the biomedical industry. It involves swallowing a small disposable video capsule that takes photographic images as it passes through the gastrointestinal (GI) tract. WCE allows physicians to visualize and diagnose disorders covering the full length of the GI tract. Although WCE can provide useful images of the internal GI tract, the identification of the exact location of the detected disease remains unclear. Location information is very crucial for the subsequent treatment of the detected disease either through surgery or through local drug delivery. The huge potential of WCE in future endoscopic practice relies on the successful tracking of the wireless capsule. This paper presents a comprehensive systematic review on the recent developments in WCE localization techniques that have been reported in credible sources, namely, IEEE Xplore, PubMed, Scopus, Science Direct, Springer Link, and Google Scholar. Detailed analysis and systematic comparison are provided to highlight the achievement and future direction of WCE localization. This paper can be a valuable source of reference and guidance for future research in this field.

Local Parametric Approach of Wireless Capsule Endoscope Localization Using Randomly Scattered Path Loss Based WCL

We propose scattered path loss based weighted centroid localization (WCL) algorithm for wireless video capsule endoscope (VCE). The main challenge in this approach is the random deviation in the measured received signal strength indicator (RSSI) caused by multipath propagation and shadowing effects of human body channel which in turn increases the localization error. To address this issue, we propose local parameter dependent path loss representation in the training phase and apply adaptive least square error (LSE) method to extract the parameters. Then, in the test phase, we estimate distance using the extracted parameters and the randomly scattered path loss. The position of capsule is estimated using non-degree based WCL followed by a calibration process. We propose suboptimal method of estimating the calibration coefficient and also compute the optimal value of coefficient analytically to set the benchmark. We develop a simulation platform using MATLAB to present the results and to verify the performance. We gradually increase the number of sensors and place them in different topologies using different dimensions. The obtained accuracy by our proposed suboptimal method of WCL is very close to the optimal benchmark for all cases. Our proposed approach also outperforms existing works in terms of localization accuracy.

Derivation of CRLB for Wireless Capsule Endoscope Localization Using Received Signal Strength

This paper presents a theoretical analysis for location estimation of a wireless capsule endoscope (WCE) in the small-intestine region. Under three different shadowing scenarios by assuming standard deviation of the shadowing with the constant variance, parameter-dependent variance, or correlated and parameter-dependent variance, analytical formulas are derived for the Cramer–Rao lower bound (CRLB) for capsule positioning when distance and azimuth angle measurements are employed to estimate the location of the capsule. The CRLB analysis can be employed to quantify the best achievable location estimation performance. We also present some numerical results to show the CRLB for three scenarios. The results show that in the most realistic scenario, when the shadowing is spatially correlated and distance dependent and the standard deviation of noise is 7 dB, the maximum square root of CRLB is 3.8 mm. Moreover, the square root of CRLB experiences its minimum value of 2 mm, when the capsule is away from its first location about half of the capsule length. Overall, the result of this paper suggests that it is possible to use received signal strength such as measured by a radar system for location estimation of a WCE.

Hybrid TOA/RSSI-Based Wireless Capsule Endoscope Localization with Relative Permittivity Estimation

Hybrid TOA/RSSI-Based Wireless Capsule Endoscope Localization with Relative Permittivity Estimation

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)).