Curvelet-based texture description to classify intact and damaged boar spermatozoa

Víctor González-Castro, Enrique Alegre, Oscar García-Olalla, Diego García-Ordás, María Teresa García-Ordás, Laura Fernández-Robles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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).

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages448-455
Number of pages8
Volume7325 LNCS
EditionPART 2
DOIs
Publication statusPublished - 27 Jul 2012
Externally publishedYes
Event9th International Conference on Image Analysis and Recognition, ICIAR 2012 - Aveiro, United Kingdom
Duration: 25 Jun 201227 Jun 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7325 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference9th International Conference on Image Analysis and Recognition, ICIAR 2012
CountryUnited Kingdom
CityAveiro
Period25/06/1227/06/12

Keywords

  • biomedical image
  • classification
  • Curvelet
  • feature extraction

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