IPASJ International Journal of Electronics & Communication (IIJEC)
Web Site: http://www.ipasj.org/IIJEC/IIJEC.htm
Email: [email protected]
ISSN 2321-5984
A Publisher for Research Motivation........
Volume 3, Issue 4, April 2015
Detection of Knee Osteoarthritis by Measuring
the Joint Space Width in Knee X-ray Images
1.
Bindushree R, 2.Sanjeev Kubakaddi , Nataraj Urs3
1
PG Scholar, Department of ECE
Reva Institute of Technology Bangalore
2
Director, ITIE Knowledge Solutions Bangalore
3
Associate Professor, Department of ECE
Reva Institute of Technology Bangalore
ABSTRACT
Osteoarthritis is a degenerative joint disease where the cartilage slowly wears away. Cartilage, which covers the bone and
makes the joint movement smoothen. Due to degradation of cartilage, joint space width will be reduced. In this project, we use
several image processing techniques such as contrast enhancement, histogram equalization, thresholding and canny edge
detection are implementing to the knee x-ray image. Measurement of joint space width is the important thing. The joint space
width (JSW) obtained is compared with standard joint space width value, 4.8 for female and 5.7 for male. From this we can
detect the osteoarthritic knee or normal knee.
Keywords: osteoarthritis, x-ray, jsw, cartilage
1. INTRODUCTION
Osteoarthritis is most common form of arthritis, which can be occurs in knee joint. The cartilage which covers the knee
bone and acts like cushion for the movement of the knee joints. The degradation of the cartilage, which results
swelling, pain and causing stiffness in the joint movement.
Knee joint is a very important joint of human body, in fact the whole weight of the body lies on the knee whether we
are standing, walking or running. It is very vulnerable joint, and more often at some stage of the life many people have
knee related problems. It affects about 6% of the adults and it can be seen more in women than in men [1]. At the age
of 40 and above, the probability of occurrence OA will be increased. Around 50% of the 65 and above population are
affected by knee osteoarthritis. It can also affect to the younger generation [2].
Causes for knee osteoarthritis [3]
 Obesity
 Heredity
 Age
 Repetitive stress injuries
 Gender
 Joint instability
 Sport stress with high impact loading
 Heavy weight lifting
In this work, many image processing techniques are used. For segmentation, we are using automatic method for extract
the joint region. Then measure the joint space width using zero crossing detectors.
Methods and results are shown in further sections. Joint space width (JSW) measurement is used as a major criterion in
the diagnosis of osteoarthritis (OA) from radiographs and for monitoring progression of the disease.
2. METHODOLOGY
The data used for this project are knee X-ray images, which belonging to different group of age, weight etc. This data
contains both normal and osteoarthritic knee X-ray images.
Methodology consists of following steps
Volume 3, Issue 4, April 2015
Page 18
IPASJ International Journal of Electronics & Communication (IIJEC)
Web Site: http://www.ipasj.org/IIJEC/IIJEC.htm
Email: [email protected]
ISSN 2321-5984
A Publisher for Research Motivation........
Volume 3, Issue 4, April 2015




Pre-processing
Thresholding
Canny edge detection
Segmentation
Input knee x-ray image
Contrast Enhancement
Histogram Equalization
Thresholding
Masking
Canny Edge Detection
Measurement of JSW
Figure 1 .Image processing steps for quantification of Joint space
2.1 Pre-processing
Image pre-processing is the term for operations on images. The aim of the pre-processing is an improvement of the
image data that suppresses undesired distortion or enhances some image features relevant for further processing and
analysis task.
2.2 Thresholding
Thresholding is the simplest method of image segmentation. Thresholding can be used to create the binary image from
the gray scale image.
The image f(x, y), composed of light objects on a dark background. To extract objects from the background is to select a
threshold, T that separates these modes. Any point (x, y) in the image at which f(x, y)>T is called an object point.
Otherwise the point is called a background point. The segmented image is given is given by
1
0
g (x, y) = 
f ( x, y )  T
f ( x, y )  T
(1)
2.3 Canny edge detection
Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in
images.
The process of canny edge detection algorithm can be divided into following steps
 Apply Gaussian filter to smooth the image in order to remove the noise
 Find the intensity gradients of the image
 Apply non-maximum suppression to get rid of spurious response to edge detection
 Apply double threshold to determine potential edges
 Track edges by hysteresis
Volume 3, Issue 4, April 2015
Page 19
IPASJ International Journal of Electronics & Communication (IIJEC)
A Publisher for Research Motivation........
Volume 3, Issue 4, April 2015
Web Site: http://www.ipasj.org/IIJEC/IIJEC.htm
Email: [email protected]
ISSN 2321-5984
2.4 Segmentation
Segmentation subdivides an image into its constituent regions or objects. Segmentation of the nontrivial images is one
of the most difficult tasks in image processing.
Categories of segmentation are
 Threshold based segmentation
 Edge based segmentation
 Region based segmentation
 Clustering techniques
 Matching
The knee X-ray Image is the input image, which is subjected to contrast enhancement for the better view of anatomical
boundaries. Contrast stretching [4] technique uses the range of intensity values it possess and span it to desired range of
values using point pre-processing to improve the contrast of a given image. Contrast stretched image is subjected to
histogram equalization. Histogram equalization [5] distributes the pixel into different gray levels. So that the
enhancement of the image takes place.
Figure 2 Input x-ray knee image
Figure 3 Contrast stretched image
Figure 4 Histogram equalized image
Volume 3, Issue 4, April 2015
Page 20
IPASJ International Journal of Electronics & Communication (IIJEC)
A Publisher for Research Motivation........
Volume 3, Issue 4, April 2015
Web Site: http://www.ipasj.org/IIJEC/IIJEC.htm
Email: [email protected]
ISSN 2321-5984
After histogram equalization, that image is subjected to thresholding. The thresholded image obtained is shown in fig5.
Figure 5 Thresholded image
For segmenting the joint space, we are generating automatic mask considering that the joint space always situated at
the center of the X-ray image. Then canny edge detection is applied to the segmented image. Canny edge detected
image obtained is shown in fig6.
Figure 6 Canny edge detected image
Finally zero crossing detectors [6] are used to measure the joint space width of the knee X-ray image.
3. CONCLUSION
This method is implemented in mat lab using some morphological operations in image processing techniques.
Automatic mask is used to segment the joint space, considering that joint space is usually situated at the center of knee
X-ray image. Joint space width obtained is compared with standard joint space width value. If JSW is greater than
standard value then that image is said to be normal case otherwise it is osteoarthritic case.
REFERENCES
[1]. J. W-P. Michael, et al., The Epidemiology, Etiology, Diagnosis, and Treatment of Osteoarthritis of the Knee,
Deutsches Ärtzeblatt International, 2010, 107(9): 152–162 (Level of evidence: 2A).
[2]. S. Coleman, et al., A randomized controlled trial of a self-management education program for osteoarthritis of the
knee delivered by health care professionals, arthritis research & amp; therapy, 2012
[3]. RK Arya, Vijay Jain, Osteoarthritis of the knee joint: An overview, Journal Indian Academy of Clinical Medicine,
2013, 14(2): 154-62 (Level of evidence: 2A)
[4]. Petrou M, Costas and P. “Image Processing: the fundamentals.” John Wiley & Sons. 2010.
[5]. Maini R & Aggarwal H. “A Comprehensive Review of Image Enhancement Techniques.” J Comput 2, 8-13, 2010.
[6]. Harini, Sanjeev Kubakaddi, “Measurement of Cartilage Thickness for Early Detection of Knee Osteoarthritis
(KOA)” IEEE Trans 2013, page no: 208-211.
Volume 3, Issue 4, April 2015
Page 21