Detection of Modic changes in MR images of spine using local binary patterns

• Autorzy: Athertya Jiyo S. , Saravana Kumar Gurunathan , Govindaraj Jayaraj

Identyfikatory

DOI

10.1016/j.bbe.2018.09.003

• Języki publikacji: EN

Abstrakty

EN

Background and objective: With increase in prevalence of lower back pain, fast and reliable computer aided methods for clinical diagnosis associated with the same is needed for improving the healthcare reach. The magnetic resonance images exhibit a change in signal intensity on the vertebral body close to end plates, which are termed as Modic changes (MC), and are known to be clear indicators of lower back pain. The current work deals with computer aided methods for automating the classification of signal changes between normal and degenerate cases so as to aid physicians in precise and suitable diagnosis for the ailment. Methods: In order to detect Modic changes in vertebrae, initially the vertebrae are segmented from sagittal MR T1 and T2 imaged using a semi automatic cellular automata based segmentation. This is followed by textural feature extraction using Local Binary Patterns (LBP) and its variants. Various classifiers based on machine learning approaches using Random Forest, kNN, Bayes and SVM were evaluated for its classification performance. Since medical image dataset in general have bias towards healthy and diseased state, data augmentation techniques were also employed. Results: The implemented method is tested and validated over a dataset containing 100 patients. The proposed framework achieves an accuracy of 81% and 91.7% with and without augmentation of data respectively. A comparative study with the state of art methods reported in literature shows that the method proposed in better in terms of computational cost without any compromise on classification accuracy. Conclusion: A novel approach to identify MC in vertebrae by exploiting textural features is proposed. This shall assist radiologists in detecting abnormalities and in treatment planning.

Słowa kluczowe

EN

modic changes; vertebral degeneration; local binary pattern; cellular automata; feature detection; data augmentation

PL

zwyrodnienie kręgosłupa; lokalne wzorce binarne; automat komórkowy; wykrywanie cech

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 1
• Strony: 17--29
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Athertya Jiyo S.

• 3D Imaging and Additive Manufacturing Lab, Department of Engineering Design, IIT – Madras, Chennai, Tamil Nadu, India

autor

Saravana Kumar Gurunathan

• Department of Engineering Design, Indian Institute of Technology, Madras, Room No. 418, Chennai 600036, Tamil Nadu, India; http://ed.iitm.ac.in/gsaravana/

autor

Govindaraj Jayaraj

• Department of Radiology, Apollo Specialty Hospitals, Chennai, Tamil Nadu, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).