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Nebojša T. Milošević
Researcher at University of Belgrade
Publications - 91
Citations - 1010
Nebojša T. Milošević is an academic researcher from University of Belgrade. The author has contributed to research in topics: Fractal analysis & Biology. The author has an hindex of 17, co-authored 77 publications receiving 854 citations. Previous affiliations of Nebojša T. Milošević include Instituto Tecnológico de Santo Domingo.
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Journal ArticleDOI
Mathematical modeling and computational analysis of neuronal cell images: Application to dendritic arborization of Golgi-impregnated neurons in dorsal horns of the rat spinal cord
Dušan Ristanović,Bratislav D. Stefanović,Nebojša T. Milošević,M. Grgurevic,Jovan B. Stanković +4 more
TL;DR: Two mathematical models of neuronal arborization patterns are developed, whose solutions yielded the inverse power-laws and generalized power-law scaling and aimed to support quantitatively the general concept of Rexed's laminar scheme of the dorsal horn of mammals.
Journal ArticleDOI
Mathematical modelling of transformations of asymmetrically distributed biological data: an application to a quantitative classification of spiny neurons of the human putamen.
TL;DR: In order to resolve the asymmetry problem in data distribution, the transformed the authors' asymmetrically distributed data into an approximately normal distribution using a family of simple power functions and on a basis of appropriate probability analysis proposes a more acceptable classification scheme for the spiny neurons.
Book ChapterDOI
The Morphology of the Brain Neurons: Box-Counting Method in Quantitative Analysis of 2D Image
TL;DR: The most popular technique of fractal analysis, i.e., the “box-counting method,” was used and image preprocessing was investigated, precisely how images at different sizes, resolutions, and rotation angles could influence in the magnitude of the box dimension.
Proceedings ArticleDOI
Box-Count Analysis of Two Dimensional Images: Methodology, Analysis and Classification
TL;DR: By using basic terms of fractal analysis and statistical assessment of correlation coefficients of a straight line fit, correct choice for the size of boxes is shown and correct box-count dimension is shown in case of neurons with sparse or thick dendrites and small or large cell bodies.
Journal ArticleDOI
On the classification of normally distributed neurons: an application to human dentate nucleus
TL;DR: It is proved that the abscissa of the point of intersection of the curves could represent the boundary between the two adjacent overlapping neuronal classes, since the error done by such division is minimal.