Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques
Resumen:
This work presents a framework for diagnosing sub-clinical endometritis, a common uterine disease in dairy cattle, based in the analysis of ultrasound images of the uterine horn. The main contribution consists in the feature extraction proposal, based on the characteristics that the expert takes into account for diagnosing, such as statistics measures, image textures, shape, custom thickness measures and histogram, among others. Given the segmentation of the different regions of the uterine horn, a fully automatic supervised classification is performed, using a model based on C-SVM. Two different datasets of ultrasound images were used, acquired and tagged by an expert. The proposed framework shows promising results, allowing to consider the development of a complete automatic procedure to measure morphological features of the uterine horn that may contribute in the diagnosis of the pathology.
| 2014 | |
| Procesamiento de Señales | |
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://hdl.handle.net/20.500.12008/41829
https://doi.org/10.1007/978-3-319-12568-8_84 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522940000927744 |
|---|---|
| author | Fernández, Alicia |
| author2 | Lecumberry, Federico Tailanian, Matias Gnemmi, Giovanni Meikle, Ana Pereira, Isabel Randall, Gregory |
| author2_role | author author author author author author |
| author_facet | Fernández, Alicia Lecumberry, Federico Tailanian, Matias Gnemmi, Giovanni Meikle, Ana Pereira, Isabel Randall, Gregory |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Fernández, Alicia Lecumberry, Federico Tailanian, Matias Gnemmi, Giovanni Meikle, Ana Pereira, Isabel Randall, Gregory |
| dc.date.accessioned.none.fl_str_mv | 2023-12-11T19:57:57Z |
| dc.date.available.none.fl_str_mv | 2023-12-11T19:57:57Z |
| dc.date.issued.es.fl_str_mv | 2014 |
| dc.date.submitted.es.fl_str_mv | 20231211 |
| dc.description.abstract.none.fl_txt_mv | This work presents a framework for diagnosing sub-clinical endometritis, a common uterine disease in dairy cattle, based in the analysis of ultrasound images of the uterine horn. The main contribution consists in the feature extraction proposal, based on the characteristics that the expert takes into account for diagnosing, such as statistics measures, image textures, shape, custom thickness measures and histogram, among others. Given the segmentation of the different regions of the uterine horn, a fully automatic supervised classification is performed, using a model based on C-SVM. Two different datasets of ultrasound images were used, acquired and tagged by an expert. The proposed framework shows promising results, allowing to consider the development of a complete automatic procedure to measure morphological features of the uterine horn that may contribute in the diagnosis of the pathology. |
| dc.identifier.citation.es.fl_str_mv | Tailanián, M, Lecumberry, F, Fernández, A, Gnemmi, G, Meikle, A, Pereira, I, Randall, G. "Dairy Cattle Sub-clinical Uterine Disease Diagnosis Using Pattern Recognition and Image Processing Techniques". Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_84 |
| dc.identifier.doi.es.fl_str_mv | https://doi.org/10.1007/978-3-319-12568-8_84 |
| dc.identifier.isbn.es.fl_str_mv | 978-3-319-12568-8 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/41829 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | Springer |
| dc.relation.ispartof.es.fl_str_mv | Bayro-Corrochano E., Hancock E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques |
| dc.type.es.fl_str_mv | Capítulo de libro |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/bookPart |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | This work presents a framework for diagnosing sub-clinical endometritis, a common uterine disease in dairy cattle, based in the analysis of ultrasound images of the uterine horn. The main contribution consists in the feature extraction proposal, based on the characteristics that the expert takes into account for diagnosing, such as statistics measures, image textures, shape, custom thickness measures and histogram, among others. Given the segmentation of the different regions of the uterine horn, a fully automatic supervised classification is performed, using a model based on C-SVM. Two different datasets of ultrasound images were used, acquired and tagged by an expert. The proposed framework shows promising results, allowing to consider the development of a complete automatic procedure to measure morphological features of the uterine horn that may contribute in the diagnosis of the pathology. |
| eu_rights_str_mv | openAccess |
| format | bookPart |
| id | COLIBRI_f70f72b36f07ff628c9e860b0684a0ba |
| identifier_str_mv | Tailanián, M, Lecumberry, F, Fernández, A, Gnemmi, G, Meikle, A, Pereira, I, Randall, G. "Dairy Cattle Sub-clinical Uterine Disease Diagnosis Using Pattern Recognition and Image Processing Techniques". Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_84 978-3-319-12568-8 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41829 |
| publishDate | 2014 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2023-12-11T19:57:57Z2023-12-11T19:57:57Z201420231211Tailanián, M, Lecumberry, F, Fernández, A, Gnemmi, G, Meikle, A, Pereira, I, Randall, G. "Dairy Cattle Sub-clinical Uterine Disease Diagnosis Using Pattern Recognition and Image Processing Techniques". Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_84978-3-319-12568-8https://hdl.handle.net/20.500.12008/41829https://doi.org/10.1007/978-3-319-12568-8_84This work presents a framework for diagnosing sub-clinical endometritis, a common uterine disease in dairy cattle, based in the analysis of ultrasound images of the uterine horn. The main contribution consists in the feature extraction proposal, based on the characteristics that the expert takes into account for diagnosing, such as statistics measures, image textures, shape, custom thickness measures and histogram, among others. Given the segmentation of the different regions of the uterine horn, a fully automatic supervised classification is performed, using a model based on C-SVM. Two different datasets of ultrasound images were used, acquired and tagged by an expert. The proposed framework shows promising results, allowing to consider the development of a complete automatic procedure to measure morphological features of the uterine horn that may contribute in the diagnosis of the pathology.Made available in DSpace on 2023-12-11T19:57:57Z (GMT). No. of bitstreams: 5 TLFGMPR14.pdf: 483037 bytes, checksum: 2998f8a514f916b0f1995b49f0b1111e (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4244 bytes, checksum: 528b6a3c8c7d0c6e28129d576e989607 (MD5) Previous issue date: 2014enengSpringerBayro-Corrochano E., Hancock E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Procesamiento de SeñalesDairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniquesCapítulo de libroinfo:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaFernández, AliciaLecumberry, FedericoTailanian, MatiasGnemmi, GiovanniMeikle, AnaPereira, IsabelRandall, GregoryProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse |
| spellingShingle | Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques Fernández, Alicia Procesamiento de Señales |
| status_str | publishedVersion |
| title | Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques |
| title_full | Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques |
| title_fullStr | Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques |
| title_full_unstemmed | Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques |
| title_short | Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques |
| title_sort | Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques |
| topic | Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/41829 https://doi.org/10.1007/978-3-319-12568-8_84 |
