Fuzzy classification improvement by a pre-perceptual labelled segmentation algorithm



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Amo, Ana del and Sobrevilla, P. and Montseny, E. and Montero, Javier (2004) Fuzzy classification improvement by a pre-perceptual labelled segmentation algorithm. In NAFIPS 2004: Ammual meeting of the north american fuzzy information processing society,vols 1and 2: fuzzy sets in the heart of the canadianI rockies. IEEE Conference Publications, 1 . IEEE, Banff, Canada, pp. 486-490. ISBN 0-7803-8376-1

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Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1336331


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.

Item Type:Book Section
Additional Information:

Annual Meeting of the North-American-Fuzzy-Information-Processing-Society
JUN 27-30, 2004

Uncontrolled Keywords:Computer Science; Artificial Intelligence; Computer Science; Information Systems
Subjects:Sciences > Computer science > Artificial intelligence
ID Code:16941
Deposited On:31 Oct 2012 09:29
Last Modified:02 Sep 2020 10:54

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