DECISION SUPPORT SYSTEM FOR BONE FRACTURE DETECTION USING IMAGE PROCESSING TECHNIQUES

Abstract

Computer Aided Diagnosis (CAD) has been a major research subject for diagnosing pathologies using computer vision and artificial intelligence. Human body is composed of 206 long, short and irregular bones. Bones are very prone to common pathology known as fractures. There are several etiologies of bone fractures. The bone fractures are of various types ranging from highly devastating comminuted fractures to hair line fractures. An algorithm has been proposed in this study to detect bone fractures using image processing techniques. For that purpose, MATLAB v. R2015a was used to execute the said task. 126 plain radiographic X ray images, acquired from a public sector hospital of Islamabad, containing long bone fractures were classified into “ground truth annotated” and their counterpart “test” data sets. The “test” images were preprocessed by contrast adjustment and noise removal followed by segmentation into background and foreground by Active Contour Model. Hough transform is applied, as a feature extraction technique, after that, for detecting vertical lines in the image which lead to identification of bone fractures. The results were calculated through Jaccard Index and finally the mean precision value for each image was calculated. The percentage precision was equal to 88.52% which is highest or equal to any bone fracture detection algorithm proposed so far up to best of our knowledge. The proposed algorithm open new grounds for CAD analysis of bone fractures, reducing the load of radiology departments of public sector hospitals.