Abstract
The assessment of boar sperm head images according to their acrosome status is a very important task in the veterinary field. Unfortunately it can only be performed manually, which is slow, non-objective and expensive. It is important to provide companies an automatic and reliable method to perform this task. In this paper a new method which uses texture descriptors based on the Curvelet Transform is proposed. Its performance has been compared with other texture descriptors based on the Wavelet transform, and also with moments based descriptors, as they seem to be successful for this problem. Texture descriptors performed better, and curvelet-based ones achieved the best hit rate (97%) and area under the ROC curve (0.99).
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González-Castro, V., Alegre, E., García-Olalla, O., García-Ordás, D., García-Ordás, M.T., Fernández-Robles, L. (2012). Curvelet-Based Texture Description to Classify Intact and Damaged Boar Spermatozoa. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_53
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DOI: https://doi.org/10.1007/978-3-642-31298-4_53
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