Segmentation of the Left Ventricle from Cardiac MR Images Based on Radial GVF Snake

Liang, Jia; Ding, Guofu; Wu, Yuwei

doi:10.1109/bmei.2008.188

Cited by 23 publications

(13 citation statements)

References 6 publications

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“…However, estimating the five parameters of the ellipse is an isolated step, which does not comply with the evolution of the snake contour. Liang et al [13] proposed a radial gradient vector flow (RGVF) snake to segment LV automatically. In [13], the ROI could be transformed into polar coordinates where myocardium looks more like a horizontal band rather than a circle.…”

Section: Related Workmentioning

confidence: 99%

“…Liang et al [13] proposed a radial gradient vector flow (RGVF) snake to segment LV automatically. In [13], the ROI could be transformed into polar coordinates where myocardium looks more like a horizontal band rather than a circle. This shape characteristic enables snake to evolve towards 1D radial direction instead of 2D image plane, which simplifies snake energy functions to 1D.…”

Section: Related Workmentioning

confidence: 99%

“…Although an impressive research effort has been devoted to automatic LV segmentation, it remains a challenging problem, mainly because of the difficulties inherent from MR cardiac images [2]. There have been extensive researches such as graph cuts [3,4], morphological operations [5,6], dynamic weights fuzzy connectedness framework [7,8], active contours or snake model [9][10][11][12][13]2] and supervised learning methods [14][15][16][17], to overcome challenges of the LV segmentation. Petitjean and Dacher [18] presented a comprehensive review of LV segmentation algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model

Wang

Jia

2013

Computer Vision and Image Understanding

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model

Wang

Jia

2013

Computer Vision and Image Understanding

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“…Lynch et al [77] presented an automated level-set scheme for the segmentation of CMR data using prior knowledge of the temporal deformation of the myocardium. Liang et al [78] proposed an automated approach using the radial GVF [79] and the Hough transform to segment the LV contours. Zhuang et al [80] proposed a framework to propagate the labels in a heart atlas to the CMR images for ventricle segmentations To overcome these limitations, we propose a novel approach to segment the LV wall borders from short-axis cine CMR images, which exploits bi-directional coupled parametric deformable models.…”

Section: Automated Methodsmentioning

confidence: 99%

Image based approach for early assessment of heart failure.

Sliman¹

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“…Lynch et al [57] presented an automated level-set scheme for the segmentation of CMR data using prior knowledge of the temporal deformation of the myocardium. Liang et al [58] proposed an automated approach using the radial GVF [73] and the Hough transform to segment the LV contours. Zhuang et al [59] proposed a framework to propagate the labels in a heart atlas to the CMR images for ventricle segmentations based on image reg- is proposed by Katouzian et al [62].…”

Section: Semimentioning

confidence: 99%

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Elnakib¹

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DEDICATIONThis dissertation is dedicated to my mother, my father, and my wife for their love, patience, and support during the completion of this endeavor.iii ACKNOWLEDGMENTSIn the Name of Allah, the most beneficent, the most merciful. All deepest thanks are due to Allah, the compassionate for the uncountable gifts given to me.I would like to express my sincere gratitude to Professor El-Baz, my dissertation advisor, for the immeasurable amount of support and guidance he has provided throughout this study. He gave me the opportunity to work in his distinguished research group and provided me with an exciting working environment that facilitate many opportunities to develop new ideas and working on promising applications. Dr. El-Baz is always willing to give, share, and appreciate. I knew that I could always expect full credit for my accomplishments. He always supported me with novel ideas that helped me to publish my works in the top international conference and journal papers. It is worthy to mention that Professor El-Baz is a great advisor as well as a good friend who helps any Ph.D. student who works with him in high energy level. I cannot thank him enough, and shall remain grateful for all he has done. Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality.This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in vi the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure-the largest fiber bundle that connects the two hemispheres in the brain-for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object's visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching...

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Segmentation of the Left Ventricle from Cardiac MR Images Based on Radial GVF Snake

Cited by 23 publications

References 6 publications

Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model

Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model

Image based approach for early assessment of heart failure.

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

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