Detection of Osteoarthritis Based on EHO Thresholding

Abstract

Knee Osteoarthritis (OA) is a joint disease that is commonly observed in people around the world. Osteoarthritis commonly affects patients who are obese and those above the age of 60. A valid knee image was generated by Computed Tomography (CT). In this work, efficient segmentation of CT images using Elephant Herding Optimization (EHO) optimization is implemented. The initial stage employs, the CT image normalization and the normalized image is incited to image enhancement through histogram correlation. Consequently, the enhanced image is segmented by utilizing Niblack and Bernsen algorithm. The (EHO) optimized outcome is evaluated in two steps. The initial step includes image enhancement with the measure of Mean square error (MSE), Peak signal to noise ratio (PSNR) and Structural similarity index (SSIM). The following step includes the segmentation which includes the measure of Accuracy, Sensitivity and Specificity. The comparative analysis of EHO provides 95% of accuracy, 94% of specificity and 93% of sensitivity than that of Active contour and Otsu threshold.