Box-Counting Method of 2D Neuronal Image: Method Modification and Quantitative Analysis Demonstrated on Images from the Monkey and Human Brain
Open Access
- 1 January 2017
- journal article
- research article
- Published by Wiley in Computational and Mathematical Methods in Medicine
- Vol. 2017, 1-9
Abstract
This study calls attention to the difference between traditional box-counting method and its modification. The appropriate scaling factor, influence on image size and resolution, and image rotation, as well as different image presentation, are showed on the sample of asymmetrical neurons from the monkey dentate nucleus. The standard BC method and its modification were evaluated on the sample of 2D neuronal images from the human neostriatum. In addition, three box dimensions (which estimate the space-filling property, the shape, complexity, and the irregularity of dendritic tree) were used to evaluate differences in the morphology of type III aspiny neurons between two parts of the neostriatum.Keywords
Funding Information
- Ministry of Education and Science Republic of Serbia (III 41031)
This publication has 20 references indexed in Scilit:
- Fractal analysis in a systems biology approach to cancerSeminars in Cancer Biology, 2011
- On the Fractal Nature of Nervous Cell SystemFrontiers in Neuroanatomy, 2011
- Application of fractal analysis to neuronal dendritic arborisation patterns of the monkey dentate nucleusNeuroscience Letters, 2007
- Fractality of dendritic arborization of spinal cord neuronsNeuroscience Letters, 2006
- Use of Fractal Theory in Neuroscience: Methods, Advantages, and Potential ProblemsMethods, 2001
- Neurons and fractals: how reliable and useful are calculations of fractal dimensions?Journal of Neuroscience Methods, 1998
- Determination of fractal dimension of physiologically characterized neurons in two and three dimensionsJournal of Neuroscience Methods, 1995
- Early dendrite development in spinal cord cell cultures: A quantitative studyJournal of Neuroscience Research, 1993
- A fractal analysis of cell imagesJournal of Neuroscience Methods, 1989
- A fast parallel algorithm for thinning digital patternsCommunications of the ACM, 1984