merging two segmentation algorithms border detection by means of laplacian of gaussian and a local threshold which uses the sauvola method sauvola and pietikainen 2000 in this method the threshold t x y is calculated using the average m x y and the standard deviation s x y of the gray pixel levels within a size window n u00d7 n centered on the pixel x y where r is the maximum value of the standard deviation r 128 for an image in gray scale k is a parameter which takes positive values between 0 2 and 0 5 one example is shown in fig6 finally the pixels which coincide both with the image obtained by means of the laplacian of gaussian as well as the local sauvola threshold will be considered as part of the binarized image fig1 and 14 said fused binarization is afterwards processed by means of the watershed algorithm which in spanish is known as a water division line which is a technique for separation of connected objects intuitively the technique may consider an image on a gray scale such as the topographic image of a terrestrial relief wherein each pixel is associated as a u201c height u201d value of its corresponding gray level in this sense one could think that the gray intensities with greater broadness correspond to plains or mountains whereas those intensities with smaller values correspond to valleys and rivers the technique additionally incorporates a flooding of the valleys process from the lowest height values local minimum values which constitute flooding basins surrounded by mountain chains up to the tallest height levels the low gray intensity areas.