Determination of osteoarthritis using histogram of oriented gradients and multiclass SVM

Determination of osteoarthritis using histogram of oriented gradients and multiclass SVM

Автор: Shivanand S. Gornale, Pooja U. Patravali, Kiran S. Marathe, Prakash S. Hiremath

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 12 vol.9, 2017 года.

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Knee Osteoarthritis is most ordinary kind of joint inflammation, which often occurs in one or both the knee joints. Osteoarthritis is additionally called as 'wear and tear' process of joint that results in dynamic disintegration of articular cartilage. Cartilage is smooth substantial layer that ensures movement to occur effortlessly. In Osteoarthritis, the cartilage is inclined towards the destruction as it loses elasticity and becomes brittle. Osteoarthritis is regularly investigated from radiographic evaluation after clinical examination. In any case, a visual evaluation made by the restorative physician depends on experience that varies subjectively and is profoundly reliant on their experience. Subsequently, in order to make diagnostic process more systematic and reliable, evolution of imaging based analysis for early recognition of Osteoarthritis is required. The objective of this study is to develop a machine vision approach for investigation of Knee Osteoarthritis using region based and active shape model. The computation involves histogram of oriented gradient (HOG) method. The processed HOG elements are computed using multiclass SVM for evaluating Osteoarthritis based on Kellgren and Lawrence (KL) grading system. The classification rate of 97.96% for Grade-0, 92.85% for Grade-1, 86.20% for Grade-2, 100% for Grade-3 & Grade-4 is obtained. The results are promising and competitive which are validated by the medical experts.

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Osteoarthritis, Knee X-ray, Active contour model, Histogram of oriented gradients, Multiclass SVM

Короткий адрес: https://sciup.org/15015923

IDR: 15015923   |   DOI: 10.5815/ijigsp.2017.12.05

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