An Adaptive Linearized Method for Localizing Video Endoscopic Capsule Using Weighted Centroid Algorithm

Video Capsule Endoscope (VCE) sends images of abnormalities in the gastrointestinal (GI) tract. While the physicians receive these images, they have little idea of their exact location which is needed for proper treatment. The proposed localization system consists of a 3D antenna array (with 8 recei...

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Bibliographic Details
Published in:International journal of distributed sensor networks 2015-01, Vol.2015 (3), p.342428
Main Authors: Hany, Umma, Wahid, Khan A.
Format: Article
Language:English
Subjects:
Algorithms
Antenna arrays
Design and construction
Diagnostic imaging
Endoscopes
Endoscopic surgery
Gastrointestinal system
Least squares method
Localization
Matlab
Medical research
Medicine, Experimental
Methods
Position (location)
Regression
Sensors
Wave propagation
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Summary:Video Capsule Endoscope (VCE) sends images of abnormalities in the gastrointestinal (GI) tract. While the physicians receive these images, they have little idea of their exact location which is needed for proper treatment. The proposed localization system consists of a 3D antenna array (with 8 receiver sensors) and one transmitter embedded inside the electronic capsule. We propose an adaptive linearized method of localization using Weighted Centroid Localization (WCL) where the position is calculated by averaging the weighted sum of the reference positions. In our proposed system, first we identify the path loss attenuation exponents using linear least square regression of the collected data (RSSI versus distance). Then the path loss model is linearized to minimize the path loss deviation which is mainly caused due to the nonhomogeneous environment of radio propagation. Then the instantaneous path loss (PL) measured by the sensors is attenuated to the above linearized model and considered as the weight of the sensors to find the location of the capsule using WCL. Finally a calibration process is applied using linear least square regression. To assess the performance, we model the path loss and implement the algorithm in Matlab for 2,530 possible positions with a resolution of 1 mm. The results show that the algorithm achieves high localization accuracy compared with other related methods when simulated using a 3D small intestine model.
ISSN:1550-1329
1550-1477
1550-1477
DOI:10.1155/2015/342428