Automatic recognition of pertrochanteric bone fractures in femur using level sets

Femur fracture is one of the common diseases in the elderly. In this paper, an automatic and efficient classification method of femoral pertrochanteric fractures is proposed based on image segmentation techniques. The types of femoral pertrochanteric fracture are defined firstly according to the difference of fracture parts. To reduce the computational complexity, only four directions images are used in each 3D femur, i.e. the anterior, the anterolateral, the posterior and the posterolateral images of a femur. And then those fracture images are segmented from background using level set method. Considering the numerical errors and the instability of evolution in conventional level set formulations, a distance regularization term and an external energy constraint term are used, which are able to maintain a desired shape of the level set function and speed up the motion of the zero level contour in evolution process. After segmentation, the Canny edge detector is used to extract the fracture edges on femur. The types of femoral pertrochanteric fracture are classified by comparing the difference of edges between reference normal images and the testing images. Except the given four directions of 3D image, the classification is processed automatically. Experimental results illustrated the effectiveness of the proposed method.