Classification of CT brain images of head trauma

Authors:
Tianxia Gong;Ruizhe Liu;Chew Lim Tan;Neda Farzad;Cheng Kiang Lee;Boon Chuan Pang;Qi Tian;Suisheng Tang;Zhuo Zhang
Affiliations:
Department of Computer Science, School of Computing, National University of Singapore, Singapore;Department of Computer Science, School of Computing, National University of Singapore, Singapore;Department of Computer Science, School of Computing, National University of Singapore, Singapore;Department of Learning, Management, Informatics & Ethics, Karolinska Institute, Stockholm, Sweden;National Neuroscience Institute, Tan Tock Seng Hospital, Singapore;National Neuroscience Institute, Tan Tock Seng Hospital, Singapore;Insitute of Infocomm Research, Singapore;Insitute of Infocomm Research, Singapore;Insitute of Infocomm Research, Singapore
Venue:
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Year:
2007

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Abstract

A method for automatic classification of computed tomography (CT) brain images of different head trauma types is presented in this paper. The method has three major steps:1. The images are first segmented to find potential hemorrhage regions using ellipse fitting, background removal and wavelet decomposition technique; 2. For each region, features (such as area, major axis length, etc.) are extracted; 3. Each extracted feature is classified using machine learning algorithm; the images are then classified based on its component regions' classification. The automatic medical image classification will be useful in building a content-based medical image retrieval system.