Latin American applied research
versión ISSN 0327-0793
Biomedical image processing is a difficult task because of the presence of noise, textured regions, low contrast and high spatial resolution. The objects to be segmented show a great variability in shape, size and intensity whose inaccurate segmentation conditions the ulterior quantification and parameter measurement. The partition of an image in regions that allow the experienced observant to obtain the necessary information can be done using a Mathematical Morphology tool called the Watershed Transform (WT). This transform is able to distinguish extremely complex objects and is easily adaptable to various kinds of images. The success of the WT depends essentially on the existence of unequivocal markers for each of the objects of interest. The standard methods of marker detection are highly specific, they have a high computational cost and they determine markers in an effective but not automatic way when processing highly textured images. This paper proposes the use of clustering techniques for the automatic detection of markers that allows the application of the WT to biomedical images. The results allow us to conclude that the method proposed is an effective tool for the application of the WT.
Palabras llave : Image Segmentation; Watershed Transform; Mathematical Morphology; Pattern Recognition.