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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)
dc.subject 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/
dc.conicet.fuente individual


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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)