title: Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ.
abstract: This paper proposes a method for assessing the acrosome state of boar spermatozoa heads using digital image processing. We use gray level images in which spermatozoa have been labeled as acrosome-intact or acrosome damaged using the information of a coupled fluorescent image. The heads are segmented obtaining the outer head contour. A set of "n" inner contours separated by a logarithmic distance function is calculated later. For each point of the, in this case, seven contours a number of local texture features are computed. We have compared the classification performance of Relevance Learning Vector Quantization, class conditional means and KNN, employing cross-validation for the evaluation. Gradient magnitude data offer the best result with an overall test error of only 1%. This result outperforms previously applied methods and suggests this approach as an interesting automatized approach to this veterinarian problem.
authors.affiliation: Department of Electrical, Systems and Automatic Engineering, University of Leon, Spain. enrique.alegre@unileon.es
authors.forename: E
authors.initials: E
authors.lastname: Alegre
authors.forename: M
authors.initials: M
authors.lastname: Biehl
authors.forename: N
authors.initials: N
authors.lastname: Petkov
authors.forename: L
authors.initials: L
authors.lastname: Sanchez
mesh_terms: D000177:Acrosome; D000818:Animals; D007091:Image Processing, Computer-Assisted; D008297:Male; D008856:Microscopy, Fluorescence; D013077:Sperm Head; D013552:Swine
keywords: Acrosome assessment; Classification; Image description; RLVQ; Texture descriptor