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.
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Gong, T. et al. (2007). Classification of CT Brain Images of Head Trauma. In: Rajapakse, J.C., Schmidt, B., Volkert, G. (eds) Pattern Recognition in Bioinformatics. PRIB 2007. Lecture Notes in Computer Science(), vol 4774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75286-8_38
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DOI: https://doi.org/10.1007/978-3-540-75286-8_38
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