Abstract
In this paper, a recent unsupervised segmentation algorithm based on computation of 2-dimensional (2D) local center of mass (LCM) technique is used to segment X-ray images of Knee Osteoarthritis (KOA) of various stages. Clinically, KOA is classified into four grades ranging from 1 to 4 based on the degradation of ligament in between the two bones fibula and femur and causes suffering with impaired movement. Identifying this space between bones through anterior view of knee X-ray image can indicate the type of KOA and helps in selection of suitable treatment process. Several segmentations methods in scrutinizing the 2D Knee X-ray image for KOA is in practice are supervised segmentation methods. Unsupervised segmentation techniques like watershed, K-means etc., are not sufficient to produce a precise segmentation results on its own and also segmented image takes more memory space. In this research, a new unsupervised segmentation algorithm based on computation of LCM technique is used to segment 2D knee X-ray images to screen the KOA stages. The calculated center of masses (CM) is related to the spatial region of pixels and forms the clusters; hence the segmentation is done based on the information from the whole image rather than only on the neighboring pixels. LCM method of segmentation is tested on 80 clinically categorized X-ray images (20 from each category) of KOA. The results are compared with two popular segmentation methods such as Watershed and Gaussian Mixed Model-Hidden Markov Random Field (GMM-HMRF) segmentation methods, respectively. Evaluated Dice score gives higher value of 0.59 +/- 0.02 for the segmented results obtained from LCM computation method. Memory space occupied by LCM based segmented images is around 250 +/- 30 kilo bytes (KB) which is less compared to other two watershed and GMM-HMRF methods. Overall, it is observed that LCM method could able to produce a crystal-clear segmented region boundaries and less memory space when compared to other two methods. The CMs calculated also depends on the intensity value of grey scale pixels and this method would be more appropriate for bone mineral density (BMD) analysis.