Prediction of Rheumatoid Arthritis disease using improved segmentation algorithm

Abstract

Image processing and segmentation algorithms have improved to a great extent in diagnosing numerous complications by analyzing Electronic Medical Records (EMR) like X-Rays, CT scan and MRI Images. However the accuracy of predicting the outcome is still a major challenge, which mainly depends on the quality of algorithms used, the testing dataset, and the segmentation process. Therefore, it is essential to identify novel algorithms for image processing to improve the accuracy of prediction. One of the important medical complication that is considered for our research is Rheumatoid Arthritis (RA). It is a persistent immune disorder that causes reduced bone density, cartilage damage and inflammations in joints. Traditional methods, though cost effective like blood test, X- Ray analysis and cell diagnosis, they have their own error factors. On the other hand techniques like advanced thermal imaging are accurate but with extremely high cost. Therefore, the proposed idea is to comparatively assess the cost, accuracy and early detection of RA based on scan images of hands and fingers. A Novel Gaussian filtering and segmentation algorithms is proposed for removing unwanted disturbances and extracting the clear images of the joints, that segments only the affected part in the image to provide a better accuracy. Gamma correction luminosity is used for the sample images which is then processed for identifying the shadows of the image. Major implication of the proposed novel algorithms helps to detect the present stages of RA for affected persons and also predict the possibility of RA at early stages.