Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/51045
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Type: Book chapter
Title: Image processing of finite size rat retinal ganglion cells using multifractal and local connected fractal analysis
Author: Jelinek, H.
Cornforth, D.
Roberts, A.
Landini, G.
Bourke, P.
Iorio, A.
Citation: AI 2004: Advances in Artificial Intelligence, 2005 / Webb, G., Yu, X. (ed./s), vol.3339, pp.961-966
Publisher: Springer
Publisher Place: Berlin
Issue Date: 2005
Series/Report no.: Lecture notes in computer science ; 3339.
ISBN: 3-540-24059-4
9783540240594
Editor: Webb, G.
Yu, X.
Abstract: Automated image processing aids in classification of biological images. Many natural structures such as neurons may be multifractal and therefore not analyzable using current methods. The multifractal spectrum proposed here may mitigate this, Here we report the outcome of applying three methods that elucidate the variation within 16 rat retinal ganglion cells using the local connected fractal dimension (LCFD), mass-radius (MR) and maximum likelihood multifractal (MLM) analyses. Our results based on LCFD indicate that the neurons studied are possibly multifractal. However utilizing the MR method provided inconclusive results due to the finite size of the cells and the density variation throughout their structure. This has been addressed by utilizing a novel unbiased method - the MLM method. To improve the our results we are now aiming to use AI algorithms to optimize the selection of parameter values associated with the MLM method.
DOI: 10.1007/978-3-540-30549-1_86
Appears in Collections:Aurora harvest
Medical Sciences publications

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