Publications
2008
1.
Alegre, Enrique; Biehl, Michael; Petkov, Nicolai; Sánchez-González, Lidia
Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ Artículo de revista
En: Computers in Biology and Medicine, vol. 38, no 4, pp. 461–468, 2008, (Publisher: Pergamon).
Resumen | Enlaces | BibTeX | Etiquetas: artificial insemination, machine learning, phase-contrast microscopy, sperm classification
@article{alegre_automatic_2008,
title = {Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ},
author = {Enrique Alegre and Michael Biehl and Nicolai Petkov and Lidia Sánchez-González},
url = {https://www.sciencedirect.com/science/article/pii/S0010482508000103},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
journal = {Computers in Biology and Medicine},
volume = {38},
number = {4},
pages = {461–468},
abstract = {This paper presents an automatic method for classifying boar sperm cells as acrosome-intact or acrosome-damaged using phase-contrast microscopy images. Sperm heads are segmented, and feature vectors based on gradient magnitude along the contour are extracted. Learning Vector Quantization (LVQ) is applied to classify 320 labeled sperm heads, achieving a 6.8% test error, which is sufficient for semen quality control in artificial insemination.},
note = {Publisher: Pergamon},
keywords = {artificial insemination, machine learning, phase-contrast microscopy, sperm classification},
pubstate = {published},
tppubtype = {article}
}
This paper presents an automatic method for classifying boar sperm cells as acrosome-intact or acrosome-damaged using phase-contrast microscopy images. Sperm heads are segmented, and feature vectors based on gradient magnitude along the contour are extracted. Learning Vector Quantization (LVQ) is applied to classify 320 labeled sperm heads, achieving a 6.8% test error, which is sufficient for semen quality control in artificial insemination.