A New Approach of Skull Fracture Detection in CT Brain Images

摘要

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, multilevel 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.