Segmentation of Inter-neurons in Three Dimensional Brain Imagery

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Author(s)
Tuxworth, Gervase
Meedeniya, Adrian
Blumenstein, Michael
Griffith University Author(s)
Year published
2010
Metadata
Show full item recordAbstract
Segmentation of neural cells in three dimensional fluorescence microscopy images is a challenging image processing problem. In addition to being important to neurobiologists, accurate segmentation is a vital component of an automated image processing system. Due to the complexity of the data, particularly the extreme irregularity in neural cell shape, generic segmentation techniques do not perform well. This paper presents a novel segmentation technique for segmenting neural cells in three dimensional images. Accuracy rates of over 90% are reported on a data set of 100 images containing over 130 neural cells and ...
View more >Segmentation of neural cells in three dimensional fluorescence microscopy images is a challenging image processing problem. In addition to being important to neurobiologists, accurate segmentation is a vital component of an automated image processing system. Due to the complexity of the data, particularly the extreme irregularity in neural cell shape, generic segmentation techniques do not perform well. This paper presents a novel segmentation technique for segmenting neural cells in three dimensional images. Accuracy rates of over 90% are reported on a data set of 100 images containing over 130 neural cells and subsequently validated using a novel data set of 64 neurons.
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View more >Segmentation of neural cells in three dimensional fluorescence microscopy images is a challenging image processing problem. In addition to being important to neurobiologists, accurate segmentation is a vital component of an automated image processing system. Due to the complexity of the data, particularly the extreme irregularity in neural cell shape, generic segmentation techniques do not perform well. This paper presents a novel segmentation technique for segmenting neural cells in three dimensional images. Accuracy rates of over 90% are reported on a data set of 100 images containing over 130 neural cells and subsequently validated using a novel data set of 64 neurons.
View less >
Journal Title
Lecture Notes in Computer Science
Volume
6474
Copyright Statement
© 2010 Springer 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
Image processing