A New Approach of Skull Fracture Detection in CT Brain Images

Authors:
Wan Mimi Wan Zaki;Mohammad Faizal Ahmad Fauzi;Rosli Besar
Affiliations:
Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia 43600;Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia 63100;Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia 75450
Venue:
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Year:
2009

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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.