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Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows a segmentation with the largest area in the cell. We study different wavelet families and we conclude that the wavelet db1 is the best and it can serve for posterior works on blood pathologies. The proposed method generates goods results when it is applied on several images. Finally, the proposed algorithm made in MatLab environment is verified for a selected blood cells.

The purpose of this paper was to study elastomer powder from crushed used tires (CUTs). In particular, the behavior of the green density of elastomeric powder was analyzed by varying compaction pressure. In the Anglo-saxon bibliography, this powder is known as ground tire rubber: ground tire rubber (GTR). The density of the tyre was made using a hydrostatic balance, the analysis of grain size using cribbing sieves, and the measures of compression parameters by means of a Universal Testing Machine. The main goal was to obtain a behavior model of ground tire rubber along different compaction pressures. This model was used to predict optimum compaction pressures in order to achieve the highest density. This was the first step to obtain recycled products when sintering processes are applied, evidently if thermal compression was used as a manufacturing process. This established model predicted the evolution of green density versus compaction pressures very accurately.