Computer-aided diagnosis of complications of total hip replacement X-ray images

Hip replacement surgery has experienced a dramatic evolution in recent years supported by the latest developments in many areas of technology and surgical procedures. Unfortunately complications that follow hip replacement surgery remains the most challenging dilemma faced both by the patients and medical experts. The thesis presents a novel approach to segment the prosthesis of a THR surgical process by using an Active Contour Model (ACM) that is initiated via an automatically detected seed point within the enarthrosis region of the prosthesis. The circular area is detected via the use of a Fast, Randomized Circle Detection Algorithm. Experimental results are provided to compare the performance of the proposed ACM based approach to popular thresholding based approaches. Further an approach to automatically detect the Obturator Foramen using an ACM approach is also presented. Based on analysis of how medical experts carry out the detection of loosening and subsidence of a prosthesis and the presence of infections around the prosthesis area, this thesis presents novel computational analysis concepts to identify the key feature points of the prosthesis that are required to detect all of the above three types of complications. Initially key points along the prosthesis boundary are determined by measuring the curvature on the surface of the prosthesis. By traversing the edge pixels, starting from one end of the boundary of a detected prosthesis, the curvature values are determined and effectively used to determine key points of the prosthesis surface and their relative positioning. After the key-points are detected, pixel value gradients across the boundary of the prosthesis are determined along the boundary of the prosthesis to determine the presence of subsidence, loosening and infections. Experimental results and analysis are presented to show that the presence of subsidence is determined by the identification of dark pixels around the convex bend closest to the stem area of the prosthesis and away from it. The presence of loosening is determined by the additional presence of dark regions just outside the two straight line edges of the stem area of the prosthesis. The presence of infections is represented by the determination of dark areas around the tip of the stem of the prosthesis. All three complications are thus determined by a single process where the detailed analysis defer. The experimental results presented show the effectiveness of all proposed approaches which are also compared and validated against the ground truth recorded manually with expert user input.