Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICMA.2013.6618049
Title: Ultrasound guided automatic localization of needle insertion site for epidural anesthesia
Authors: Yu, S.
Tan, K.K. 
Shen, C.
Sia, A.T.H.
Keywords: Automatic Identification
Epidural Anesthesia
Local Normalization
Template Matching
Ultrasound Imaging Guidance
Issue Date: 2013
Citation: Yu, S.,Tan, K.K.,Shen, C.,Sia, A.T.H. (2013). Ultrasound guided automatic localization of needle insertion site for epidural anesthesia. 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013 : 985-990. ScholarBank@NUS Repository. https://doi.org/10.1109/ICMA.2013.6618049
Abstract: In this paper, ultrasound imaging is utilized to detect the anatomical structure of the lumbar spine based on which an image processing algorithm will search for key features to identify the optimal needle insertion site. The key challenge lies in the nature of ultrasound images which are obscure and have low spatial resolution, induced by contamination from random speckle noises. In order to improve the interpretability of ultrasound images, a modified version of local normalization using the Difference of Gaussian algorithm is first used for pre-processing to filter the speckle noise and extract the main anatomical structure in the raw images obtained. Meanwhile, local means induced by non-uniform wave reflection rate is also successfully removed by the proposed pre-processing algorithm, thus a potential element that may degrade the image recognition accuracy is excluded. In the second stage, a template matching algorithm, augmented with a position correlation function, automatically identifies the key features of interest and thus the insertion site. The approach has been tested on more than 200 ultrasound images with a 100% success rate. The proposed system allows the anesthetist to use the approach efficiently without the burden of interpreting real time ultrasound images. © 2013 IEEE.
Source Title: 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013
URI: http://scholarbank.nus.edu.sg/handle/10635/84336
ISBN: 9781467355582
DOI: 10.1109/ICMA.2013.6618049
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