DETECTION OF HAIRLINE MANDIBULAR FRACTURE USING MAX-FLOW MIN-CUT AND KOLMOGOROV-SMIRNOV DISTANCE

The paper addresses a clinically challenging problem of hairline mandibular fracture detection from Computed Tomography (CT) images. A hairline fracture that has critical clinical importance, can be easily missed due to lack of sharp discontinuity and presence of intensity inhomogeneity, if not scrutinized carefully. In this work, 2D image slices containing mandibles with hairline fractures are separated first from input image sequences of broken craniofacial skeletons. This is achieved through an intensity-based image retrieval scheme with Kolmogorov-Smirnov distance as the measure of similarity and an unbroken mandible as the reference image. 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. Existing graph cut-based segmentation framework is enhanced with a novel construction of the flow network, guided by the geometry of the mandible. Ford-Fulkerson algorithm is employed next to obtain a minimum cut, which represents the hairline fracture in these already separated slices. Experimental results demonstrate the effectiveness of the proposed method. Index Terms — Hairline mandibular fracture, Max-flow mincut, Kolmogorov-Smirnov distance