Left Ventricular Segmentation in MR Using Hierarchical Multi-class Multi-feature Fuzzy Connectedness

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

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004 (2004), pp. 402-410.

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cardiac, mri, segmentation

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.

@incollection{citeulike:2783830, 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.}, author = {Pednekar, Amol and Kurkure, Uday and Muthupillai, Raja and Flamm, Scott and Kakadiaris, Ioannis A. }, citeulike-article-id = {2783830}, journal = {Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004}, keywords = {cardiac, mri, segmentation}, pages = {402--410}, posted-at = {2008-05-11 07:25:43}, priority = {2}, title = {Left Ventricular Segmentation in MR Using Hierarchical Multi-class Multi-feature Fuzzy Connectedness}, url = {http://www.springerlink.com/content/m0fpk8xwq4y49y5n }, year = {2004} }

RIS record

TY - CHAP ID - citeulike:2783830 L3 - citeulike-article-id:2783830 TI - Left Ventricular Segmentation in MR Using Hierarchical Multi-class Multi-feature Fuzzy Connectedness JF - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004 SP - 402 EP - 410 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. KW - cardiac KW - mri KW - segmentation AU - Pednekar, Amol AU - Kurkure, Uday AU - Muthupillai, Raja AU - Flamm, Scott AU - Kakadiaris, Ioannis PY - 2004/// UR - http://www.springerlink.com/content/m0fpk8xwq4y49y5n ER -

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