Automated bone segmentation from Pelvic CT images

Abstract

Segmentation of bone tissue from pelvic CT images is a crucial step in developing an automated system for assisting experts with diagnostic decisions for traumatic pelvic injuries. The method proposed in this paper combines wavelet processing, Laplacian filtering, morphology operations, a series of region growing techniques and gradient based segmentation methods to create an automated segmentation system. The method, tested against a database of pelvic injury CT images, provides promising results. This computationally efficient method sets the grounds for creating an automated decision making system that will be able to provide physicians with reliable recommendations for the treatment of traumatic pelvic injuries.