Study of Segmentation Techniques for Assessment of Osteoarthritis in Knee X-ray Images

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Shivanand S. Gornale 1,* Pooja U. Patravali 1 Archana M. Uppin 2 P. S. Hiremath 3

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

2. JNMC, Belgavi-Karnataka India

3. KLE Technological University, Hubballi-Karnataka India

* Corresponding author.


Received: 17 Sep. 2018 / Revised: 6 Nov. 2018 / Accepted: 18 Dec. 2018 / Published: 8 Feb. 2019

Index Terms

Osteoarthritis, Knee X-ray, Sobel edge detection, Prewitt edge detection, Otsu’s Segmentation, Texture based Segmentation, k-Nearest Neighbor


Arthritis is one of the chronic joint disorders that have affected many lives including middle age and older age group. Arthritis exists in many forms and one among them is Osteoarthritis. Osteoarthritis affects the bigger joints like knee, hip, spine, feet etc. Early detection of Osteoarthritis is most essential if not treated properly may result in deformity. The researchers have become more concerned to detect the disorder in the early stage by merging their medical knowledge with machine vision approach in an appropriate way. The objective of this work is to study various segmentation techniques for the detection of Osteoarthritis in the early stage. The different segmentation technique like Sobel and Prewitt edge segmentation, Otsu’s method of segmentation and Texture based segmentation are used to carry out the experimentation. The different statistical features are computed, analyzed and classified. The accuracy rate of 91.16% for Sobel method, 96.80% for Otsu’s method, 94.92% for texture method and 97.55% for Prewitt method is obtained. The results are more promising and competitive which are validated by medical experts.

Cite This Paper

Shivanand S. Gornale, Pooja U. Patravali, Archana M. Uppin, Prakash S. Hiremath, "Study of Segmentation Techniques for Assessment of Osteoarthritis in Knee X-ray Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.2, pp. 48-57, 2019. DOI: 10.5815/ijigsp.2019.02.06


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