Self-organizing maps
Computers in Biology and Medicine
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Curvelet-based texture description to classify intact and damaged boar spermatozoa
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Texture and moments-based classification of the acrosome integrity of boar spermatozoa images
Computer Methods and Programs in Biomedicine
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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.