Two Robust Techniques for Segmentation of Biomedical Images
Dos Técnicas Robustas para la Segmentación de Imágenes Biomédicas
Roberto Rodríguez, Patricio J. Castillo, Valia Guerra1, Ana G. Suárez and Ebroul Izquierdo2
Digital Signal Processing Group
1 Numerical Methods Group ]]>
Institute of Cybernetics, Mathematics & Physics (ICIMAF)
2 Department of Electronic Engineering, Queen Mary, London University
E–mails: rrm@icmf.inf.cu, vguerra@icmf.inf.cu, anagloria@icmf.inf.cu
Article received on October 15, 2004; accepted on August 18, 2006
Abstract
Image segmentation plays an important role in many systems of computer vision. According to criterions of many authors the segmentation finishes when it satisfies the goals of the observer. For that reason, an only method there is not able of solving all the problems that exists in the present time. In this work, we carry out a comparison between two segmentation techniques; namely, through the mean shift, where we give a new algorithm, and by using spectral methods. In the paper we discuss, through examples with biomedical real images, the advantages and disadvantages of them.
Keywords: Image segmentation, mean shift, spectral methods.
]]>Resumen
La segmentación de imagen juega un importante rol en muchos sistemas de visión por computadora. Acorde al criterio de muchos autores la segmentación finaliza cuando son satisfechos los objetivos del observador. Por esa razón, no hay un único método capaz de resolver todos los problemas que existen en la época actual. En este trabajo, nosotros llevamos a cabo una comparación entre dos técnicas de segmentación; a saber, a través de la media desplazada, donde nosotros ofrecemos un nuevo algortimo, y usando los métodos espectrales. En el artículo nosotros discutimos, a través de ejemplos con imágenes biomédicas reales, las ventajas y desventajas de ellos.
Palabras claves: Segmentación de imagen, media desplazada, métodos espectrales.
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