Título:
Detection of follicles in ultrasound videos of bovine ovaries
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Autor:
Gómez, Alvaro
Carbajal, Guillermo
Fuentes, Magdalena
Viñoles, Carolina
Carbajal, Guillermo
Fuentes, Magdalena
Viñoles, Carolina
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Tipo de documento:
Ponencia
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Palabras clave:
Follicle detection
Cascade classifier
Multitracking
Cascade classifier
Multitracking
Descriptores:
Procesamiento de Señales
Año de publicación:
2017
Contenido:
Resumen:
Ultrasound imaging is a veterinarian standard procedure for the monitoring of ovarian structures in cattle. Recent studies, suggest that the number of antral follicles can give a cue of the future fertility of a specimen. Therefore, there has been a growing interest in counting the number of antral follicles at early stages in life. In the most typical procedure, the operator performs a trans-rectal ultrasound scan and counts the follicles on the live video that is seen in the ultrasound machine. This is a challenging task and requires highly trained experts that can reliably detect and count the follicles in a quick sweep of a few seconds. This work presents the integration of several signal processing techniques to the problem of automatically detecting follicles in ultrasound videos of bovine cattle ovaries. The approach starts from an ultrasound video that traverses the ovary from end to end. Putative follicle regions are detected on each frame with a cascade of boosted classifiers. In order to impose temporal coherence, the detections are tracked across the frames with multiple Kalman filters. The tracks are analyzed to separate follicle detections from other false detections. The method is tested on a phantom dataset of ovaries in gelatin with dissection ground truth. Results are promising and encourage further extension to in-vivo ultrasound videos.
Descripción:
21st Iberoamerican Congress, CIARP 2016, Lima, Peru, 8–11, nov. 2016,
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Springer
EN:
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2016. Lecture Notes in Computer Science, vol 10125. Springer, Cham. https://doi.org/10.1007/978-3-319-52277-7_43
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Citación:
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2016. Lecture Notes in Computer Science(), vol 10125. Springer, Cham. https://doi.org/10.1007/978-3-319-52277-7_43
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Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| GCFV17.pdf | — | 1.98 MB | Adobe PDF |