Left ventricular segmentation in MR using hierarchical multi-class multi-feature fuzzy connectedness

Amol Pednekar; Uday Kurkure; Raja Muthupillai; Scott Flamm; Ioannis A. Kakadiaris

doi:10.1007/978-3-540-30135-6_49

Left ventricular segmentation in MR using hierarchical multi-class multi-feature fuzzy connectedness

Amol Pednekar, Uday Kurkure, Raja Muthupillai, Scott Flamm, Ioannis A. Kakadiaris

Research output: Contribution to journal › Conference article › peer-review

9 Scopus citations

Abstract

In this paper, we present a new method for data-driven automatic extraction of endocardial and epicardial contours of the left ventricle in cine bFFE MR images. Our method employs a hierarchical, multi-class, multi-feature fuzzy connectedness framework for image segmentation. This framework combines image intensity and texture information with anatomical shape, while preserving the topological relationship within and between the interrelated anatomical structures. We have applied this method on cine bFFE MR data from eight asymptomatic and twelve symptomatic volunteers with very encouraging qualitative and quantitative results.

Original language	English (US)
Pages (from-to)	402-410
Number of pages	9
Journal	Lecture Notes in Computer Science
Volume	3216
Issue number	PART 1
DOIs	https://doi.org/10.1007/978-3-540-30135-6_49
State	Published - 2004
Event	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France Duration: Sep 26 2004 → Sep 29 2004

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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abstract = "In this paper, we present a new method for data-driven automatic extraction of endocardial and epicardial contours of the left ventricle in cine bFFE MR images. Our method employs a hierarchical, multi-class, multi-feature fuzzy connectedness framework for image segmentation. This framework combines image intensity and texture information with anatomical shape, while preserving the topological relationship within and between the interrelated anatomical structures. We have applied this method on cine bFFE MR data from eight asymptomatic and twelve symptomatic volunteers with very encouraging qualitative and quantitative results.",

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AU - Pednekar, Amol

AU - Kurkure, Uday

AU - Muthupillai, Raja

AU - Flamm, Scott

AU - Kakadiaris, Ioannis A.

PY - 2004

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N2 - In this paper, we present a new method for data-driven automatic extraction of endocardial and epicardial contours of the left ventricle in cine bFFE MR images. Our method employs a hierarchical, multi-class, multi-feature fuzzy connectedness framework for image segmentation. This framework combines image intensity and texture information with anatomical shape, while preserving the topological relationship within and between the interrelated anatomical structures. We have applied this method on cine bFFE MR data from eight asymptomatic and twelve symptomatic volunteers with very encouraging qualitative and quantitative results.

AB - In this paper, we present a new method for data-driven automatic extraction of endocardial and epicardial contours of the left ventricle in cine bFFE MR images. Our method employs a hierarchical, multi-class, multi-feature fuzzy connectedness framework for image segmentation. This framework combines image intensity and texture information with anatomical shape, while preserving the topological relationship within and between the interrelated anatomical structures. We have applied this method on cine bFFE MR data from eight asymptomatic and twelve symptomatic volunteers with very encouraging qualitative and quantitative results.

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Left ventricular segmentation in MR using hierarchical multi-class multi-feature fuzzy connectedness

Abstract

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