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. 2020 Feb 26;15(2):e0227894.
doi: 10.1371/journal.pone.0227894. eCollection 2020.

The application of key feature extraction algorithm based on Gabor wavelet transformation in the diagnosis of lumbar intervertebral disc degenerative changes

Tao Yang  1 , Renzhi Li  2   3 , Ning Liang  4 , Jing Li  5 , Yi Yang  1 , Qian Huang  6 , Yuedan Li  7 , Wei Cao  1 , Qian Wang  8 , Hongxin Zhang  1
Affiliations

The application of key feature extraction algorithm based on Gabor wavelet transformation in the diagnosis of lumbar intervertebral disc degenerative changes

Tao Yang et al. PLoS One. .

Retraction in

Abstract

Objective: Based on the theoretical basis of Gabor wavelet transformation, the application effects of feature extraction algorithm in Magnetic Resonance Imaging (MRI) and the role of feature extraction algorithm in the diagnosis of lumbar vertebra degenerative diseases were explored.

Method: The structure of lumbar vertebra and degenerative changes were respectively introduced to clarify the onset mechanism and pathological changes of lumbar vertebra degenerative changes. Most importantly, the theoretical basis of Gabor wavelet transformation and the extraction effect of feature information in lumbar vertebra MRI images were introduced. The differentiation effects of feature information extraction algorithm on annulus fibrosus and nucleus pulposus were analyzed. In this study, the data of lumbar spine MRI was randomly selected from the Wenzhou Lumbar Spine Research Database as research objects. A total of 130 discs were successfully fitted, and 109 images were graded by a doctor after observation, which was compared with the results of the artificial diagnosis. Through the comparison with the results of observation and diagnosis by professional doctors, the accuracy of feature extraction algorithm based on Gabor wavelet transformation in the diagnosis of lumbar vertebra degenerative changes was analyzed.

Results: 1. Compared with the results of the manual diagnosis, the accuracy of the classification method was 88.3%. In addition, the specificity (SPE), accuracy (ACC), and sensitivity (SEN) of the classification method were respectively 89.5%, 92.4%, and 87.6%. 2. The mutual information method and the KLT algorithm were utilized for vertebral body tracking. The maximum mutual information method was more effective in the case of fewer image sequences; however, with the increase of image frames, the accumulation of errors would make the tracking effects of images get worse. Based on the KLT algorithm, the enhanced vertebral boundary information was selected; the soft tissues showed in the obtained images were smooth, the boundary information of vertebral body was enhanced, and the results were more accurate.

Conclusion: The feature extraction algorithm based on Gabor wavelet transformation could easily and quickly realize the localization of the lumbar intervertebral disc, and the accuracy of the results was ensured. In addition, from the aspect of vertebral body tracking, the tracking effects based on the KLT algorithm were better and faster than those based on the maximum mutual information method.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The differentiation of annulus fibrosus and nucleus pulposus.
(a) The annulus fibrosus, (b) The nucleus pulposus.
Fig 2
Fig 2. The information extraction process of intervertebral disc positions.
(a) The boundary of intervertebral disc based on Gabor, (b) The detection of boundary points, (c) The elliptical fitting.
Fig 3
Fig 3. Tracking results of the maximum mutual information method.
(a) The 1st frame, (b) The 45th frame.
Fig 4
Fig 4. Tracking results of the KLT algorithm.
(a) The 1st frame, (b) The 45th frame.
See this image and copyright information in PMC

References

    1. Tetsuhiro I, Watanabe Atsuya, Kamoda Hiroto, et al. Evaluation of Lumbar Intervertebral Disc Degeneration Using T1ρ and T2 Magnetic Resonance Imaging in a Rabbit Disc Injury Model[J]. Asian Spine Journal, 2018, 12(2):317–324. 10.4184/asj.2018.12.2.317 - DOI - PMC - PubMed
    1. Bates NM, Tian Jing, Smiddy William E, et al. Relationship between the morphology of the foveal avascular zone, retinal structure, and macular circulation in patients with diabetes mellitus[J]. Scientific Reports, 2018, 8(1):5355 10.1038/s41598-018-23604-y - DOI - PMC - PubMed
    1. Haq IU, Nagoaka R, Makino T, et al. 3D Gabor wavelet based vessel filtering of photoacoustic images[J]. Conf Proc IEEE Eng Med Biol Soc, 2016:3883–3886. 10.1109/EMBC.2016.7591576 - DOI - PubMed
    1. Ou X, Pan Wei, Zhang Xu, et al. Skin Image Retrieval Using Gabor Wavelet Texture Feature[J]. International Journal of Cosmetic Science, 2016, 38(6):607–614. 10.1111/ics.12332 - DOI - PubMed
    1. Soltani Bozchalooi I, Liang M. Rebuttal to “On the distribution of the modulus of Gabor wavelet coefficients and the upper bound of the dimensionless smoothness index in the case of additive Gaussian noises: Revisited” by Dong Wang, Qiang Zhou, and Kwok-Leung Tsui[J]. Journal of Sound Vibration, 2018, 420:393–400.

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