Publications
2018
1.
Petkov, Nicolai; Alegre, Enrique; Biehl, Michael; Sánchez-González, Lidia
LVQ acrosome integrity assessment of boar sperm cells Book Section
En: Computational Modelling of Objects Represented in Images. Fundamentals, Methods and Applications, pp. 337–342, CRC Press, 2018.
Resumen | Enlaces | BibTeX | Etiquetas: artificial insemination, LVQ, sperm classification
@incollection{petkov_lvq_2018,
title = {LVQ acrosome integrity assessment of boar sperm cells},
author = {Nicolai Petkov and Enrique Alegre and Michael Biehl and Lidia Sánchez-González},
url = {https://www.taylorfrancis.com/chapters/edit/10.1201/9781315106465-57/lvq-acrosome-integrity-assessment-boar-sperm-cells-nicolai-petkov-enrique-alegre-michael-biehl-lidia-s%C3%A1nchez},
year = {2018},
date = {2018-01-01},
booktitle = {Computational Modelling of Objects Represented in Images. Fundamentals, Methods and Applications},
pages = {337–342},
publisher = {CRC Press},
abstract = {This study aims to classify boar sperm cells as acrosome-intact or acrosome-reacted using optical phase-contrast microscope images. The sperm heads are segmented, and features are extracted based on the gradient magnitude. A Learning Vector Quantization (LVQ) system with three prototypes classifies the cells, achieving an error rate of 0.165, sufficient for quality control in artificial insemination.},
keywords = {artificial insemination, LVQ, sperm classification},
pubstate = {published},
tppubtype = {incollection}
}
This study aims to classify boar sperm cells as acrosome-intact or acrosome-reacted using optical phase-contrast microscope images. The sperm heads are segmented, and features are extracted based on the gradient magnitude. A Learning Vector Quantization (LVQ) system with three prototypes classifies the cells, achieving an error rate of 0.165, sufficient for quality control in artificial insemination.