A new approach of skull fracture detection in CT brain images

Abstract

This work demonstrates a new automated approach to segment skull from 2D-CT brain image to detect any fracture case. The key steps in the proposed approach include image normalization, centroid identification, multi-level global segmentation and skull skeletonization. Feature vectors such as location and fracture size are then extracted to represent fracture cases. Twenty eight encephalic fracture images are queried from a database of 3032 normal and fractured CT brain images to evaluate the usefulness of the skull segmentation as well as the extracted feature vectors in content-based medical image retrieval system (CBMIR). Retrieval performance of Normalized Euclidean and Normalized Manhattan distance metrics show almost perfect average recall-precision plots that portray the suitability of this approach to the CBMIR of fracture cases. © 2009 Springer-Verlag.