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Chesnal D. Arepalli

dblp:135/1042 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%
Artificial intelligence
1 paper
Segmentation and scene understanding · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Segmentation and scene understanding
medical image segmentation
0.212014
A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution · IEEE Trans. Image Process. 2014
Medical and health informatics › medical imaging › medical image analysis
cardiac image segmentation
0.212014
A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution · IEEE Trans. Image Process. 2014
Medical and health informatics › medical imaging
medical image analysis
0.212014
A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution · IEEE Trans. Image Process. 2014

Methods — techniques the papers use, named apart from their topics

shape segmentation · 0.4contour evolution · 0.4coarse-to-fine strategy · 0.4
YearPublicationVenuePosition
2014 A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution
abstract
The left ventricular myocardium plays a key role in the entire circulation system and an automatic delineation of the myocardium is a prerequisite for most of the subsequent functional analysis. In this paper, we present a complete system for an automatic segmentation of the left ventricular myocardium from cardiac computed tomography (CT) images using the shape information from images to be segmented. The system follows a coarse-to-fine strategy by first localizing the left ventricle and then deforming the myocardial surfaces of the left ventricle to refine the segmentation. In particular, the blood pool of a CT image is extracted and represented as a triangulated surface. Then, the left ventricle is localized as a salient component on this surface using geometric and anatomical characteristics. After that, the myocardial surfaces are initialized from the localization result and evolved by applying forces from the image intensities with a constraint based on the initial myocardial surface locations. The proposed framework has been validated on 34-human and 12-pig CT images, and the robustness and accuracy are demonstrated.
Liangjia Zhu, Yi Gao 0002, Vikram V. Appia, Anthony J. Yezzi, Chesnal D. Arepalli, Tracy L. Faber, Arthur E. Stillman, Allen R. Tannenbaum
IEEE Trans. Image Process.5