Classification and Quantification Based on Image Analysis for Sperm Samples with Uncertain Damaged/Intact Cell Proportions View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008

AUTHORS

Lidia Sánchez , Víctor González , Enrique Alegre , Rocío Alaiz

ABSTRACT

Classifying damaged-intact cells in a semen sample presents the peculiarity that the test class distribution is unknown. This paper studies under which design conditions the misclassification rate is minimum for the uncertainty region of interest (ratio of damaged cells lower than 20%) and (b) deals with quantifying the proportion of damaged/intact sperm cells in a given sample based on computer vision and supervised learning. We have applied a discrete wavelet transform to the spermatozoa head images and computed the mean and standard deviation (WSF) and four Haralick descriptors (WCF). Using a backpropagation neural network, the error rate averaged over distributions in the region of interest is 4.85% with WCF. The assessment of several quantification methods shows the conditions under which the Adjusted Count method leads to an overall mean absolute error of 3.2 and the Classify & Count method yields 2.4, both with WCF features. Deviations of this order are considered reasonable for this field. More... »

PAGES

827-836

References to SciGraph publications

Book

TITLE

Image Analysis and Recognition

ISBN

978-3-540-69811-1
978-3-540-69812-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-69812-8_82

DOI

http://dx.doi.org/10.1007/978-3-540-69812-8_82

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1032407356


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