Lumbar spine discs labeling using axial view MRI based on the pixels coordinate and gray level features

Ala S. Al Kafri, Sud Sudirman, Abir J. Hussain, Paul Fergus, Dhiya Al-Jumeily, Hiba Al Smadi, Mohammed Khalaf, Mohammed Al-Jumaily, Wasfi Al-Rashdan, Mohammad Bashtawi, Jamila Mustafina

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Disc herniation is a major reason for lower back pain (LBP), a health issue that affects a very high proportion of the UK population and is costing the UK government over £1.3 million per day in health care cost. Currently, the process to diagnose the cause of LBP involves examining a large number of Magnetic Resonance Images (MRI) but this process is both expensive in terms time and effort. Automatic labeling of lumbar disc pixels in the MRI to detect the herniation area will reduce the time to diagnose and detect the cause of LBP by the physicians. In this paper, we present a method for automatic labeling of the lumbar spine disc pixels in axial view MRI using pixels locations and gray level as features. Clinical MRIs are used for the training and testing of the method. The pixel classification accuracy and the quality of the reconstructed disc images are used as the main performance indicators for our method. Our experiments show that high level of classification accuracy of 91.1% and 98.9% can be achieved using Weighted KNN and Fine Gaussian SVM classifiers respectively.
    Original languageEnglish
    Title of host publicationIntelligent Computing Methodologies
    EditorsDe-Shuang Huang, Abir Hussain, Kyungsook Han, M. Michael Gromiha
    PublisherSpringer-Verlag
    Pages107-116
    Number of pages10
    ISBN (Print)9783319633145
    DOIs
    Publication statusPublished - 2017
    Event13th International Conference Intelligent Computing Theories and Application - Liverpool, United Kingdom
    Duration: 7 Aug 201710 Aug 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10363 LNAI
    ISSN (Print)0302-9743

    Conference

    Conference13th International Conference Intelligent Computing Theories and Application
    Abbreviated titleICIC 2017
    CountryUnited Kingdom
    CityLiverpool
    Period7/08/1710/08/17

    Fingerprint Dive into the research topics of 'Lumbar spine discs labeling using axial view MRI based on the pixels coordinate and gray level features'. Together they form a unique fingerprint.

  • Cite this

    Al Kafri, A. S., Sudirman, S., Hussain, A. J., Fergus, P., Al-Jumeily, D., Al Smadi, H., Khalaf, M., Al-Jumaily, M., Al-Rashdan, W., Bashtawi, M., & Mustafina, J. (2017). Lumbar spine discs labeling using axial view MRI based on the pixels coordinate and gray level features. In D-S. Huang, A. Hussain, K. Han, & M. M. Gromiha (Eds.), Intelligent Computing Methodologies (pp. 107-116). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10363 LNAI). Springer-Verlag. https://doi.org/10.1007/978-3-319-63315-2_10