Abstract
A fully automated method for extracting brain structures from computed tomography images by employing adaptive filtering and finite Gaussian Mixture Modeling (GMM) with context-based enhancement is proposed. Generally, the method is composed of two phases. First, adaptive partial mean filter for noise removal and edge sharpening is used. The second phase is the multistage segmentation. Initial segmentation step concerning brain extraction from skull and non-brain tissue defines a region of interest (ROI) for further processing. Each pixel in ROI is assigned to one of three semantically fundamental classes - white matter (WM), gray matter (GM) and cerebrospinal fluid (CBF) and two extended classes of specific tissue. GMM with expectation-maximization algorithm (EM) is employed to assign initial class labels to image pixels and followed by context information modeling through Contextual Bayesian Relaxation Labeling (CBRL). The CBRL algorithm incorporates local neighborhood information and iteratively refines the outcome of GMM classification. The results of proposed approach have been verified by extracting susceptible-to-stroke regions (SSR) processed for hypodensity distribution estimation. The extracted structures are more smooth and reliable in comparison to region growing segmentation results.
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Rutczyńska, A., Przelaskowski, A., Jasionowska, M., Ostrek, G. (2010). Method of Brain Structure Extraction for CT-Based Stroke Detection. In: Piȩtka, E., Kawa, J. (eds) Information Technologies in Biomedicine. Advances in Intelligent and Soft Computing, vol 69. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13105-9_14
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DOI: https://doi.org/10.1007/978-3-642-13105-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13104-2
Online ISBN: 978-3-642-13105-9
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