Al-kafri, A, Sudirman, S, Hussain, A, Al-Jumeily, D, Al-Jumaily, M, Al-Rashdan, W, Bashtawi, M and Fergus, P (2017) Lumbar Spine Discs Labeling using Axial View MRI Based on the Pixels Coordinate and Gray Level Features. In: Lecture Notes in Computer Science . pp. 107-116. (2017 International Conference on Intelligent Computing, 07 August 2017 - 10 August 2017, Liverpool, UK).

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Abstract

Disc herniation is a major reason for lower back pain (LBP), it cost the United Kingdom (UK) government over £1.3 million per day. In fact a very high proportion of the UK population will complain from their back pain. Fur-thermore, Magnetic Resonance Imaging (MRI) is one of the main diagnosing procedure for LBP. Automatic disc labeling in the MRI to detect the herniation area will reduce the required time to issue the report from the radiologist. We present a method for automatic labeling for the lumbar spine disc area using the axial view MRI based on the pixels coordinate and gray level features. We use a clinical MRI for the training and testing. Moreover, the accuracy and the recon-structed images was the main indicator for our result. The highest achieved ac-curacy was 98.9 and 91.1 for Weighted KNN and Fine Gaussian SVM respec-tively.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	The final publication is available at Springer via https://doi.org/10.1007/978-3-319-63315-2_10
Uncontrolled Keywords:	08 Information And Computing Sciences
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General)
Divisions:	Computer Science & Mathematics
Publisher:	Springer Verlag (Germany)
Date Deposited:	05 May 2017 10:57
Last Modified:	16 May 2024 11:41
DOI or ID number:	10.1007/978-3-319-63315-2_10
URI:	https://researchonline.ljmu.ac.uk/id/eprint/6366

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