Article

Blood Cells Images- Based on Chaos Theory

Research Journal of Applied Sciences, Engineering and Technology 01/2009;
Source: DOAJ

ABSTRACT The fractal functions are considered a good choice to represent natural tissue surfaces. W hich werechosen because of the importance of short period characteristics of classifying images since the fractaldimension of a surface has an approximation of complete relation with surface toughness. Therefore, the ideaof using a new group of fractal characteristics is utilized to differentiate medical images. This workdemonstrates the technique of using chaos theory and principles of fractal engineering in the processes ofdescribing and differentiating medical images of red blood cells and while blood cells. A group consisted oftwo fractal characteristics, which are the fractal dimension, and the Lacunarity are developed to describe anddifferentiate medical images. An alternative method of box counting and mass radius is implemented tocalculate those two fractal characteristics of images surface Furthermore, an instructional program designedby using PowerPoint and includes three instructional modules depending on system? approach knowledge basedof using chaos theory and fractal engineering m the medical applications.

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    ABSTRACT: Many biological objects appear to have self-similar structures which can be characterized by their fractal dimension D. However, applications of the concept of fractal geometry are rather scarce in cell and tissue biology. Here we adapt and analyse critically 3 methods of digital image analysis to measure D of cellular profiles. As prototype examples we investigate in detail 2 samples of cells: (i) human T-lymphocytes from normal donors, and (ii) hairy leukemic cells. It is shown that D correlates to the structural complexity of the individual cell contour. The calculated D values for cells out of the same cell line scatter around a mean value D = 1.15 for T-lymphocytes (S.D. = 0.03) and D = 1.34 for hairy leukemic cells (S.D. = 0.04). Consequently, we interprete D as a statistical measure for the sample's fractal dimension.
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