Shivanand S. Gornale; Pooja U. Patravali; P. S. Hiremath

Early Detection of Osteoarthritis based on Cartilage Thickness in Knee X-ray Images

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Author(s)

Shivanand S. Gornale ^1,* Pooja U. Patravali ¹ P. S. Hiremath ²

1. Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi. Karnataka-India

2. Dept of MCA, KLE Technological University, Hubballi-Karnataka India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2019.09.06

Received: 11 Jun. 2019 / Revised: 20 Jun. 2019 / Accepted: 1 Jul. 2019 / Published: 8 Sep. 2019

Index Terms

Knee X-ray, Osteoarthritis (OA), ROI, Cartilage area, Decision tree, K-NN

Abstract

Arthritis is a joint disorder featuring inflammation. There are numerous forms of Arthritis. Arthritis essentially causes joint dis-functioning which may further tend to cause deformity and disability. Osteoarthritis (OA) is one form of arthritis which is mostly seen in old age group. A patient suffering from OA needs to visit medical experts where clinical and radiographic examination is carried out. Analysis of bone structures in initial stage is bit complex. So any vague conclusion drawn from the radiographic images may make the treatment faulty and troublesome. Thus to overcome this we have developed an algorithm that computes the cartilage area/thickness using various shape descriptors. The computed descriptors obtained the accuracy of 99.81% for K-nearest neighbour classifier and 95.09% for decision tree classifier. The estimated cartilage thickness is validated by radiographic experts as per KL grading framework which will be helpful to the doctors for quick and appropriate analysis of ailment in the early stage. The results are competitive and promising as reported in the literature.

Cite This Paper

Shivanand S. Gornale, Pooja U. Patravali, Prakash S. Hiremath, "Early Detection of Osteoarthritis based on Cartilage Thickness in Knee X-ray Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.9, pp. 56-63, 2019. DOI: 10.5815/ijigsp.2019.09.06

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International Journal of Image, Graphics and Signal Processing (IJIGSP)