Comput Methods Programs Biomed - Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ.

Tópicos

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Resumo

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.

Resumo Limpo

paper propos method assess acrosom state boar spermatozoa head use digit imag process use gray level imag spermatozoa label acrosomeintact acrosom damag use inform coupl fluoresc imag head segment obtain outer head contour set n inner contour separ logarithm distanc function calcul later point case seven contour number local textur featur comput compar classif perform relev learn vector quantiz class condit mean knn employ crossvalid evalu gradient magnitud data offer best result overal test error result outperform previous appli method suggest approach interest automat approach veterinarian problem

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