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  • Fractal Analysis for Symmetry Plane Detection in Neuroimages

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    Author(s)
    Jayasuriya, Surani Anuradha
    Liew, Alan Wee-Chung
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Jayasuriya, Surani A.
    Year published
    2013
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    Abstract
    Despite the considerable amount of research, brain symmetry plane detection is still an open problem. In this paper, we present a novel method for symmetry plane detection in magnetic resonance (MR) neuroimages based on the textural information and underlying brain's physiological structure. Fractal dimension and lacunarity analysis are used to locate the symmetry plane of the brain. The method was tested on MR data while analyzing the robustness against intensity non-uniformity, noise, and pathology. The proposed method does not need skull-stripping like pre-processing of MR images. The method was compared with another ...
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    Despite the considerable amount of research, brain symmetry plane detection is still an open problem. In this paper, we present a novel method for symmetry plane detection in magnetic resonance (MR) neuroimages based on the textural information and underlying brain's physiological structure. Fractal dimension and lacunarity analysis are used to locate the symmetry plane of the brain. The method was tested on MR data while analyzing the robustness against intensity non-uniformity, noise, and pathology. The proposed method does not need skull-stripping like pre-processing of MR images. The method was compared with another commonly used technique. The results were evaluated by an expert. The experimental results show the viability of our approach.
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    Conference Title
    PATTERN RECOGNITION AND IMAGE ANALYSIS, IBPRIA 2013
    Volume
    7887
    DOI
    https://doi.org/10.1007/978-3-642-38628-2_21
    Copyright Statement
    © 2013 Springer-Verlag Berlin Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
    Subject
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/53370
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    • Conference outputs

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