Boar spermatozoa classification using longitudinal and transversal profiles (LTP) descriptor in digital images

Abstract

A new textural descriptor, named Longitudinal and Transversal Profiles (LTP), has been proposed. This descriptor was used to classify 376 images of dead spermatozoa heads and 472 images of alive ones. The result obtained with this descriptor has been compared with the Pattern spectrum, Flusser, Hu, and a descriptor based on statistical values of the histogram. The features vectors computed have been classified using a back-propagation Neural Network and the kNN (k Nearest Neighbours) algorithm. Classification error obtained with LTP was 30.58% outperforming the other descriptors. The area under the ROC curve (AUC) has also been calculated confirming that the performance of the proposed descriptor is better that of the other texture descriptors. © 2011 Springer-Verlag Berlin Heidelberg.