Computer Aided Fracture Detection System

In the last decades, - the advancements in computer aided diagnosis (CAD systems, enables the medical practitioners in delivering timely treatments by interpreting the medical images in short duration. Analyzing X-ray images is one of the pivotal task of CADe systems. This paper presents, two new methods to effectively detect and locate the fracture in digital X-ray images. The two methods include: (i) Hough transform based fracture detection (HTBFD) an unsupervised learning approach where, fuzzy c-means thresholding, and edge detection methods are used to obtain the bone boundaries. Finally, The hough transform is utilized to detect the fracture. (ii) Gradient feature based fracture detection (GFBFD), a supervised learning approach where, Gradient features are extracted by sub-window search. Based on the region of extraction, features are labelled as a fracture/non fracture. Finally, fractures are detected on trained radial basis function neural network and K-nearest neighbor (K-NN) classifiers. The proposed methods are validated over 180 X-ray images. The experimental results shows that the radial basis function neural network gives the better recognition rate of 88% compared with KNN and HTBFD. Hence, can be used as an efficient tool for detecting and localizing the fracture.