Computer Vision Based Hairline Mandibular Fracture Detection from Computed Tomography Images

Abstract

This chapter addresses the problem of detection of hairline mandibular fractures from a sequence of computed tomography (CT) images. It has been observed that such a fracture can be easily overlooked during manual detection due to the absence of sharp surface and contour discontinuities and the presence of intensity inhomogeneity in the CT images. In this work, the 2D CT image slices of a mandible with hairline fractures are first identified from the input sequence of a fractured craniofacial skeleton. Two intensity-based image retrieval schemes with different measures of similarity, namely the Jaccard index and the Kolmogorov–Smirnov distance, are applied for that purpose. In the second part, we detect a hairline fracture in the previously identified subset of images using the maximum flow-minimum cut algorithm. Since a hairline fracture is essentially a discontinuity in the bone contour, we model it as a minimum cut in an appropriately weighted flow network constructed using the geometry of the human mandible. The Ford–Fulkerson algorithm with Edmonds–Karp refinement is employed to obtain a minimum cut. Experimental results demonstrate the effectiveness of the proposed method.