Detection of pelvic fractures using graph cuts and curvatures

Traumatic injury of the pelvis is common and potentially devastating, with pelvic fractures being a major cause of trauma patient mortality. Detection and management of pelvic injuries is challenging due to varying injury patterns and resulting complications such as hemorrhage and infection. In this paper, we investigate the application of computer-aided detection (CAD) techniques for pelvic fracture detection. We propose a fast semi-automated method of pelvic fracture detection using a combination of (i) graph cuts and (ii) mean and Gaussian curvatures. A fracture is modeled as a minimum cut in a weighted graph. The same fracture is alternatively modeled as a valley based on the signs of mean and Gaussian curvatures. Each of these methods, in isolation, generates false positives in addition to the true fracture. We then combine the two methods and perform a neighborhood analysis to eliminate the false positives. Experimental results indicate that proposed method is very promising.