Classification of Long Bone X-ray Images using New features and Support Vector Machine

Bones are protecting many organs in the human body such as the lungs, brain, heart and other internal organs. Bone fracture is a common problem in human beings and may occur due to the high pressure that is applied to the bones as a result of an accident or any other reasons. X-ray (radiograph) is the noninvasive medical experimentthat helps doctors diagnose and present medical conditions. X-rays represent the oldest and most often used kind of medical imagery. Medical image processing attempts to enhance the bone fracture diagnosis processes by creating an automated system that can go through a large database of the X-ray images and identify the required diagnosis faster and with high accuracy than the regular diagnosis processes. In this paper, the lower leg bone (Tibia) fracture is studied and many novel features are extracted using various image processing techniques. The purpose of this research is to use new investigated features and classify the X-ray bone images as a fractured and non-fractured bone and make the system more applicable and closer to the user using the Graphical User Interface (GUI). The Tibia bone fracture detection system was developed in three main steps: the preprocessing step, feature extraction using wavelet analysis, gradient analysis, principal components (PCA), and edge detection methods and classification using Support Vector Machine (SVM). The results were produced using four possible outcomes from the confusion matrix which are TP, TN, FP, and FN. The classification process was repeated using different feature groups at a time and the resultant accuracies were ranged between 70%-80%.