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dc.contributor.author
Orlando, José Ignacio
dc.contributor.author
Prokofyeva, Elena
dc.contributor.author
Blaschko, Matthew B.
dc.date.available
2018-09-10T18:12:00Z
dc.date.issued
2017-01
dc.identifier.citation
Orlando, José Ignacio; Prokofyeva, Elena; Blaschko, Matthew B.; A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images; Institute of Electrical and Electronics Engineers; Ieee Transactions On Bio-medical Engineering; 64; 1; 1-2017; 16-27
dc.identifier.issn
0018-9294
dc.identifier.uri
http://hdl.handle.net/11336/58924
dc.description.abstract
Goal: In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model. Methods: Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Results: Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available datasets: DRIVE, STARE, CHASEDB1, and HRF. Additionally, a quantitative comparison with respect to other strategies is included. Conclusion: The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean, and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood-based approach. Significance: Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Blood Vessel Segmentation
dc.subject
Conditional Random Fields (Crfs)
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Fundus Imaging
dc.subject
Structured Output Support Vector Machine (Sosvm)
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-09-10T13:09:59Z
dc.journal.volume
64
dc.journal.number
1
dc.journal.pagination
16-27
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Orlando, José Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Prokofyeva, Elena. Inserm; Francia
dc.description.fil
Fil: Blaschko, Matthew B.. Katholikie Universiteit Leuven; Bélgica
dc.journal.title
Ieee Transactions On Bio-medical Engineering
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TBME.2016.2535311
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/7420682/


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