NDLI: Segmentation of the right ventricle in MRI images using a dual active shape model

Content Provider	IET Digital Library
Author	Rewaidy, Hossam El Ibrahim, El Sayed Fahmy, Ahmed S
Abstract	Active shape models (ASM) showed to have potential for segmenting the right ventricle (RV) in cardiac magnetic resonance images (MRIs). Nevertheless, the large variability and complexity of the RV shape do not allow for concisely capturing all possible shape variations among patients and anatomical cross-sections. Noticeably, the latter increases the number of iterations required to converge to a proper solution and reduces the segmentation accuracy. In this study, the authors propose a new ASM framework that can model the RV shape in short-axis cardiac MRI images. In this framework, the RV contour is split into two simpler segments, septal (SP) and free wall, whose shape variations are independently modelled using two separate (dual) ASM models. The contour splitting is done at the location of the RV insertion points into the SP wall. Further, instead of using the conventional Procrustes method, the RV contours are aligned using the Bookstein coordinate transformation, which uses the RV insertion points as landmarks to linearly align the RV contours. The results from a dataset of 14 patients show that the proposed framework outperforms the conventional ASM framework and can model complex RV shape variation with more accuracy and in less iteration steps.
Starting Page	717
Ending Page	723
Page Count	7
ISSN	17519659
Volume Number	10
e-ISSN	17519667
Issue Number	Issue 10, Oct (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/10/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0073
Journal	IET Image Processing
Publisher Date	2016-05-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ASM Framework Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Bookstein Coordinate Transformation Cardiac Magnetic Resonance Image Computer Vision And Image Processing Technique Dual Active Shape Model Image Segmentation Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Procrustes Method Right Ventricle Segmentation RV Shape Complexity RV Shape Variability Short-axis Cardiac MRI Image Spectroscopy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Graph cuts segmentation based on multi-dimensional features for the right ventricle in cardiac MRI

Invert-U-Net DNN segmentation model for MRI cardiac left ventricle segmentation

Multilabel statistical shape prior for image segmentation

Cardiac image segmentation by random walks with dynamic shape constraint

Automatic and optimal segmentation of the left ventricle in cardiac magnetic resonance images independent of the training sets

Label fusion method based on sparse patch representation for the brain MRI image segmentation

AnnularCut: a graph-cut design for left ventricle segmentation from magnetic resonance images

Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model With Adaptation on Short-Axis Cardiac MRI

Left ventricular segmentation based on a parallel watershed transformation towards an accurate heart function evaluation

Segmentation of the right ventricle in MRI images using a dual active shape model

Similar Documents

Graph cuts segmentation based on multi-dimensional features for the right ventricle in cardiac MRI

Invert-U-Net DNN segmentation model for MRI cardiac left ventricle segmentation

Multilabel statistical shape prior for image segmentation

Cardiac image segmentation by random walks with dynamic shape constraint

Automatic and optimal segmentation of the left ventricle in cardiac magnetic resonance images independent of the training sets

Label fusion method based on sparse patch representation for the brain MRI image segmentation

AnnularCut: a graph-cut design for left ventricle segmentation from magnetic resonance images

Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model With Adaptation on Short-Axis Cardiac MRI

Left ventricular segmentation based on a parallel watershed transformation towards an accurate heart function evaluation

Segmentation of the right ventricle in MRI images using a dual active shape model