Neural networks and SVM-based classification of leukocytes using the morphological pattern spectrum. Ramirez-Cortes, J., M., Gomez-Gil, P., Alarcon-Aquino, V., Gonzalez-Bernal, J., & Garcia-Pedrero, A. Studies in Computational Intelligence, 312:19-35, 2010.
abstract   bibtex   
In this paper we present the morphological operator pecstrum, or pattern spectrum, as a feature extractor of discriminating characteristics in microscopic leukocytes images for classification purposes. Pecstrum provides an excellent quantitative analysis to model the morphological evolution of nuclei in blood white cells, or leukocytes. According to their maturity stage, leukocytes have been classified by medical experts in six categories, from myeloblast to polymorphonuclear corresponding to the youngest and oldest extremes, respectively. A feature vector based on the pattern spectrum, normalized area, and nucleus - cytoplasm area ratio, was tested using a multilayer perceptron neural network trained by backpropagation, and a Support Vector Machine algorithm. Results from Euclidean distance and k-nearest neighbor classifiers are also reported as reference for comparison purposes. A recognition rate of 87% was obtained in the best case, using 36 patterns for training and 18 for testing, with a three-fold validation scheme. Additional experiments exploring larger databases are currently in progress. © 2010 Springer-Verlag Berlin Heidelberg.
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 title = {Neural networks and SVM-based classification of leukocytes using the morphological pattern spectrum},
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 year = {2010},
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 pages = {19-35},
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 abstract = {In this paper we present the morphological operator pecstrum, or pattern spectrum, as a feature extractor of discriminating characteristics in microscopic leukocytes images for classification purposes. Pecstrum provides an excellent quantitative analysis to model the morphological evolution of nuclei in blood white cells, or leukocytes. According to their maturity stage, leukocytes have been classified by medical experts in six categories, from myeloblast to polymorphonuclear corresponding to the youngest and oldest extremes, respectively. A feature vector based on the pattern spectrum, normalized area, and nucleus - cytoplasm area ratio, was tested using a multilayer perceptron neural network trained by backpropagation, and a Support Vector Machine algorithm. Results from Euclidean distance and k-nearest neighbor classifiers are also reported as reference for comparison purposes. A recognition rate of 87% was obtained in the best case, using 36 patterns for training and 18 for testing, with a three-fold validation scheme. Additional experiments exploring larger databases are currently in progress. © 2010 Springer-Verlag Berlin Heidelberg.},
 bibtype = {article},
 author = {Ramirez-Cortes, Juan Manuel and Gomez-Gil, Pilar and Alarcon-Aquino, Vicente and Gonzalez-Bernal, Jesus and Garcia-Pedrero, Angel},
 journal = {Studies in Computational Intelligence}
}

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