Graph Cuts Framework for Kidney Segmentation with Prior Shape Constraints

Ali, Asem; Farag, Aly A.; El-Baz, Ayman

doi:10.1007/978-3-540-75757-3_47

Cited by 72 publications

(55 citation statements)

References 9 publications

(9 reference statements)

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“…A graph cut method using parametric shape priors for segmenting the LV was given in Zhu-Jacquot and Zabih [48], while Ben Ayed et al [49] employ a discrete distribution-matching energy. In other methods, Ali et al [50] construct a shape prior with a certain degree of variability and use it to segment DCE-MR kidney images.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.

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Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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“…The advantage of these approaches is that the classification of a tissue voxel is based on the actual tissue response to the bolus wash-in and wash-out. Alternatively, active contours [1] and related methods employing region and boundary properties in combination with shape constraints [3] have been used. In this work, we apply the temporal tissue response and minimal boundary length as shape information for the segmentation.…”

Section: ) Registrationmentioning

confidence: 99%

“…They are nonlinearly related to the observed signal intensities, but can be estimated by voxelwise measurements of the pre-contrast relaxation rate R1, since the relation between R1 and [Gd] is linear for low [Gd]. The acquisition parameters for all examinations were TR/TE/FA = 2.41ms/0.87ms/12 • , matrix size = 256 × 256, FOV = 425mm, voxelsize = 1.66 × 1.66 × 3mm 3 , and number of time points = 55. Dotarem was used as a contrast agent.…”

Section: A Imaging Datamentioning

confidence: 99%

Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys

Hodneland

Hanson

Lundervold

et al. 2014

IEEE Trans. on Image Process.

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Abstract-Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the kidneys requires proper motion correction and segmentation to enable an estimation of glomerular filtration rate through pharmacokinetic modeling. Traditionally, co-registration, segmentation, and pharmacokinetic modeling have been applied sequentially as separate processing steps. In this paper, a combined 4D model for simultaneous registration and segmentation of the whole kidney is presented. To demonstrate the model in numerical experiments, we used normalized gradients as data term in the registration and a Mahalanobis distance from the time courses of the segmented regions to a training set for supervised segmentation. By applying this framework to an input consisting of 4D image time series, we conduct simultaneous motion correction and two-region segmentation into kidney and background. The potential of the new approach is demonstrated on real DCE-MRI data from ten healthy volunteers.

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“…• A future work of this dissertation is to investigate the integration of the proposed work with the BioImaging lab work for the early detection of acute renal rejection [128,129,[288][289][290][291][292][293][294][295][296][297][298][299][300][301][302].…”

Section: B Directions For Future Researchmentioning

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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Graph Cuts Framework for Kidney Segmentation with Prior Shape Constraints

Cited by 72 publications

References 9 publications

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys

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

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