Osteoarthritis detection by applying quadtree analysis to human joint knee X-ray imagery

ABSTRACT

Knee Osteoarthritis (OA) is considered as one of the most popular diseases for elder people with the age of 60 and upper. In addition, there are over 10 million people in Thailand were affected by Knee OA. Age and weight are the two main risk factors to make people have knee OA. When OA has appeared to the patient, it is totally difficult to recovery back as the normal. Thus, knee OA early detection is the most important factor to prevent knee OA. The typical way to detect and analyze OA is the X-ray imaging application. This research study is directed to the early detection of OA by applying image processing (specifically the shape analysis) applied with classification techniques to knee X-ray imagery. The basic idea of the work is to find a region of interest, use shape decomposition technique and build a classifier that can later classify between OA or non-OA imageries. Firstly, the quadtree decomposition is applied to X-ray images for analyzing the regions of interest (ROI) of the knee X-ray image. There are three data sets include (i) whole knee (Dataset 1), (ii) knee joint space ROI (Dataset 2), and (iii) the application Otsu's method to knee joint space ROI (Dataset 3). Secondly, feature selection is adopted to this work in order to reduce the feature space in terms of the number of values and dimensions. Lastly, the classifier generator is applied to generate the desired classifieds which can be used to classify knee images between non-OA (normal case) and OA. The challenge of the study is how to know which ROI and the threshold value in shape decomposition are suitable to use for the classification process. The data were obtained 128 male and female participants were used for the evaluation while OA imagery was presented as 66 images. The results obtained show that the Dataset 3 sub-image is the most appropriate region of interest to consider come along with the threshold value of 10. The best classification performance in term of an Area Under the ROC Curve (AUC) value of 0.917 was recorded.

KEYWORDS:

• Shape analysis
• osteoarthritis
• knee OA
• image classification

Acknowledgments

We would like to give special thanks to Mr Sirisak Yaisoongnern for the information of the data collection process and million thanks to MD. Chaowakon Saehang for sharing his valuable knowledge in OA grading.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Sophal Chan

Sophal Chan is doing mater degree of science in information of technology at College of Computing, Prince of Songkla University, Phuket Campus.

Kwankamon Dittakan

Kwankamon Dittakan is a lecturer at Prince of Songkla University since 2005. Her research interests are focused on Knowledge Discovery in Data (or Database) and more specifically Data Mining. She has been conducting research in the field of KDD since 2011.The initial work was image classification as her Ph.D. thesis. She obtained her Ph.D. from Department of Computer Science, University of Liverpool, UK.

Subhieh El Salhi

Subhieh El Salhi obtained her B.Sc. in computer science from the Hashemite University in 2000, her M.Sc. degree from the University of Jordan in 2003, and her Ph.D. degree from the University of Liverpool, Liverpool, UK in 2014. She is currently an assistant professor at the department of Computer Information System, Faculty of Prince al Hussein bin Abdullah II for Information Technology, Hashemite University. Her research interests include algorithm design, machine learning and applied data mining, classification techniques, 3D surface representation, feature extraction for non-standard data sets, large data sets and statistical analysis in the context of data mining field.