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SUMMARY:We have developed a new algorithm to calculate the fractal dimension of dog kidney proximal convoluted tubule section. This algorithm is intermediate between box-counting and perimeter-stepping algorithms. The result of this new algorithm and correlations found between fractal dimensions and other Euclidian values of these tubuli, suggest that this algorithm is adequate to calculate fractal dimensions by points.

The effect of prenatal malnutrition on the anatomy of the corpus callosum was assessed in adult rats (45-52 days old). In the prenatally malnourished animals we observed a signifi cant reduction of the corpus callosum total area, partial areas, and perimeter, as compared with normal animals. In addition, the splenium of corpus callosum (posterior fi fth) showed a signifi cant decrease of fi ber diameters in the myelinated fi bers without changing density. There was also a signifi cant decrease in diameter and a signifi cant increase in density of unmyelinated fi bers. Measurements of perimeter's fractal dimensions from sagittal sections of the brain and corpus callosum did not show signifi cant differences between malnourished and control animals. These fi ndings indicate that cortico-cortical connections are vulnerable to the prenatal malnutrition, and suggest this may affect interhemispheric conduction velocity, particulary in visual connections (splenium).

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The possibility of characterizing the proximal convoluted tubuli of the dog kidney by means of a unique and objective value, is an attractive idea in order to automate its recognition in anatomy and patology. For this, we obtained a fractal dimension of the proximal convoluted tubuli in the dog kidney by mean box-counting method. The fractal value we obtained is a flat dimension, and it is different than the line fractal dimension of the dog kidney arterial pattern, which was previously calculated by us (Gil et al., 2006). Thus, from optical microscopy images, we are able to obtain a single quantitative measure to discriminate these dog kidney components. This measures can use to automate its recognition. Fractal geometry provides many advantages when examining the complex microscopical images of natural objects.