Journal ArticleDOI
A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes.
TLDR
This paper reviews state-of-the-art literature on vascular segmentation with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA) and discusses the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones.About:
This article is published in Medical Image Analysis.The article was published on 2009-12-01. It has received 951 citations till now. The article focuses on the topics: Scale-space segmentation & Segmentation-based object categorization.read more
Citations
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Journal ArticleDOI
Medical image analysis
Baba C. Vemuri,James S. Duncan +1 more
TL;DR: Medical imaging systems: Physical principles and image reconstruction algorithms for magnetic resonance tomography, ultrasound and computer tomography (CT), and applications: Image enhancement, image registration, functional magnetic resonance imaging (fMRI).
Patent
Method and system for patient-specific modeling of blood flow
TL;DR: In this article, a system for determining cardiovascular information for a patient is described, which includes at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the heart based on the patient specific data.
Journal ArticleDOI
Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics
TL;DR: No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.
Journal ArticleDOI
Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images
TL;DR: The proposed infinite active contour model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map, and outperforms its competitors when compared with other widely used unsupervised and supervised methods.
Journal ArticleDOI
Deep learning for cardiac image segmentation: A review
Chen Chen,Chen Qin,Huaqi Qiu,Giacomo Tarroni,Giacomo Tarroni,Jinming Duan,Wenjia Bai,Daniel Rueckert +7 more
TL;DR: In this article, a review of deep learning-based segmentation methods for cardiac image segmentation is provided, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound.
References
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A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
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A note on two problems in connexion with graphs
TL;DR: A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.
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Snakes : Active Contour Models
TL;DR: This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
Book ChapterDOI
A New Approach to Linear Filtering and Prediction Problems
TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.