Título
Neural networks and SVM-based classification of leukocytes using the morphological pattern spectrum
Autor
JUAN MANUEL RAMIREZ CORTES
MARIA DEL PILAR GOMEZ GIL
VICENTE ALARCON AQUINO
JESUS ANTONIO GONZALEZ BERNAL
ANGEL MARIO GARCIA PEDRERO
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
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.
Editor
Springer-Verlag Berlin Heidelberg
Fecha de publicación
2010
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Estudiantes
Investigadores
Público en general
Sugerencia de citación
Ramirez-Cortes, J.M., et al., (2010). Neural networks and SVM-based classification of leukocytes using the morphological pattern spectrum, P. Melin et al. (Eds.): Soft Comp. for Recogn. Based on Biometrics, SCI (312): 19–35.
Repositorio Orígen
Repositorio Institucional del INAOE
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