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oai:docta.ucm.es:20.500.14352/531672024-11-26T16:38:47Zcom_20.500.14352_14col_20.500.14352_21

00925njm 22002777a 4500 dc Del Amo, Ana author Sobrevilla, P. author Montseny, E. author Montero De Juan, Francisco Javier author 2004 The goal of this paper is to present how two different image processing approaches can be enhanced by merging both methodologies. We will see how the results of a perceptual labelled segmentation methodology [7] can be improved by applying a fuzzy classification algorithm [2] based on a fuzzy outranking methodology [9] as a postprocessing algorithm, and viceversa. A comparison of the individual algorithms with the combination of both algorithms will be presented in order to demonstrate the improvement. Color Bone Marrow (1) images will be used. The objective is to detect White Blood Cells. The detection of white blood cells in bone marrow microscopic images presents big difficulties because of the great variance in their characteristics and also because of staining and illumination inconsistences. On the other hand, the maturity classes of white blood cells actually represents a continuum; cells frequently overlap each other, and there is a fairly wide variation in size and shape of nucleus and cytoplasm regions within given cell classes. 0-7803-8376-1 10.1109/NAFIPS.2004.1336331 https://hdl.handle.net/20.500.14352/53167 https//doi.org/10.1109/NAFIPS.2004.1336331 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1336331 Fuzzy classification improvement by a pre-perceptual labelled segmentation algorithm