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)
Year published
2013
Metadata
Show full item recordAbstract
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 ...
View more >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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View more >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.
View less >
Conference Title
PATTERN RECOGNITION AND IMAGE ANALYSIS, IBPRIA 2013
Volume
7887
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