Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, 2011
In this chapter, a novel approach to align an image of a textured object with a given prototype w... more In this chapter, a novel approach to align an image of a textured object with a given prototype will be proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov���Gibbs random field (MGRF) with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed ...
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2003
... and enhancement [I]. Such models are traditionally low level stochastic models of the limited... more ... and enhancement [I]. Such models are traditionally low level stochastic models of the limited complexity. ... 191 CA Bouman and B. Liu, “Multiple resolution segmentation of textured images”, IEEE, Trans. ... “Parameter estimation in Gibbs-Markov image models,'' 61h international ...
2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007
Abstract Autism is a developmental disorder characterized by social deficits, impaired communicat... more Abstract Autism is a developmental disorder characterized by social deficits, impaired communication, and restricted and repetitive patterns of behavior. Recent neuropathological studies of autism have revealed abnormal anatomy of the cerebral white matter (CWM) in autistic brains. In this paper we introduced a novel approach to classify autistic from normal subjects based on a new shape analysis of cerebral white matter gyrifications for both normal and autistic subjects. The proposed shape analysis technique consists of three ...
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008
A new approach to align an image of a medical object with a given prototype (reference object) is... more A new approach to align an image of a medical object with a given prototype (reference object) is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a new variant rotation and scaling Markov-Gibbs random field with pairwise interaction model. Similarity to the prototype (reference object) is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype (reference object) using our previous Linear Combination of Discrete Gaussians (LCDG) probabilistic model. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.
Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, 2011
Objects of specific shapes in an image are typically segmented with a deformable model being zero... more Objects of specific shapes in an image are typically segmented with a deformable model being zero level of a geometric-level set function specifying sign-alternate shortest distances to the object boundary from each pixel. The goal shapes are approximated by a linear combination of such 2D distance maps built for mutually aligned images of given training objects. Unfortunately, the approximate shapes may deviate much from the training ones because the space of the distance maps is not closed with respect to linear operations ...
2007 IEEE International Conference on Image Processing, 2007
We propose novel Expectation Maximization (EM) based algorithms for accurate approximation of an ... more We propose novel Expectation Maximization (EM) based algorithms for accurate approximation of an empirical probability distribution of discrete scalar data. The algorithms refine our previous ones in that they approximate the empirical distribution with a linear combination of discrete Gaussians (LCDG). The use of the DGs results in closer approximation and considerably better convergence to a local likelihood maximum compared to previously involved conventional continuous Gaussian densities. Experiments in segmenting multimodal medical images show the proposed algorithms produce more adequate region borders.
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008
A new approach to align an image of a textured object with a given prototype (learned reference o... more A new approach to align an image of a textured object with a given prototype (learned reference object) is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype (learned reference object) is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. To get accurate appearance model, we developed a new approach to automatically select the most important cliques (neighborhood system) that describe the visual appearance of a texture object. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008
We propose a novel parametric deformable model controlled by shape and visual appearance priors l... more We propose a novel parametric deformable model controlled by shape and visual appearance priors learned from a training subset of co-aligned images of goal objects. The shape prior is derived from a linear combination of vectors of distances between the training boundaries and their common centroid. The appearance prior considers gray levels within each training boundary as a sample of a Markov-Gibbs random field with pairwise interaction. Spatially homogeneous interaction geometry and Gibbs potentials are analytically estimated from the training data. To accurately separate a goal object from an arbitrary background, empirical marginal gray level distributions inside and outside of the boundary are modeled with adaptive linear combinations of discrete Gaussians (LCDG). The evolution of the parametric deformable model is based on solving an Eikonal partial differential equation with a new speed function which combines the prior shape, prior appearance, and current appearance models. Due to the analytical shape and appearance priors and a simple Expectation-Maximization procedure for getting the object and background LCDG, our segmentation is considerably faster than most of the known geometric and parametric models. Experiments with various goal images confirm the robustness, accuracy, and speed of our approach.
2008 19th International Conference on Pattern Recognition, 2008
The importance of accurate early diagnostics of dyslexia that severely affects the learning abili... more The importance of accurate early diagnostics of dyslexia that severely affects the learning abilities of children cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We explore a possibility of distinguishing between dyslexic and normal (control) brains by a quantitative shape analysis of CWM gyrifications on 3D Magnetic Resonance (MR) images. Our approach consists of (i) segmentation of the CWM on a 3D brain image using a deformable 3D boundary; (ii) extraction of gyrifications from the segmented CWM, and (iii) shape analysis to quantify thickness of the extracted gyrifications and classify dyslexic and normal subjects. The boundary evolution is controlled by two probabilistic models of visual appearance of 3D CWM: the learned prior and the current appearance model. Initial experimental results suggest that the proposed 3D texture analysis is a promising supplement to the current techniques for diagnosing dyslexia.
Procedings of the British Machine Vision Conference 2007, 2007
A new approach to align an image of a textured object with a given prototype is proposed. Visual ... more A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex 2D/3D objects better than popular conventional algorithms.
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
We propose a modified Expectation-Maximization algorithm that approximates an empirical probabili... more We propose a modified Expectation-Maximization algorithm that approximates an empirical probability density function of scalar data with a linear combination of Gaussians (LCG). Due to both positive and negative components, the LCG approximates inter-class transitions more accurately than a conventional mixture of only positive Gaussians. Experiments in segmenting multi-modal medical images show the proposed LCG-approximation results in more adequate region borders.
Abstract. A new approach to align an image of a textured object with a given prototype under its ... more Abstract. A new approach to align an image of a textured object with a given prototype under its monotone photometric and affine geometric transformations is experimentally compared to more conventional registration algorithms. The approach is based on measuring similarity between the image and prototype by Gibbs energy of characteristic pairwise co-occurrences of the equalized image signals. After an initial alignment, the affine transformation maximizing the energy is found by gradient search. Experiments confirm that our ...
Abstract. We present a fast algorithm for automatic extraction of a 3D cerebrovascular system fro... more Abstract. We present a fast algorithm for automatic extraction of a 3D cerebrovascular system from time-of-flight (TOF) magnetic resonance angiography (MRA) data. Blood vessels are separated from background tissues (fat, bones, or grey and white brain matter) by voxel-wise clas- ...
Abstract. Deformable or active contour, and surface models are power-ful image segmentation techn... more Abstract. Deformable or active contour, and surface models are power-ful image segmentation techniques. We introduce a novel fast and robust bi-directional parametric deformable model which is able to segment re-gions of intricate shape in multi-modal greyscale images. ...
International angiology : a journal of the International Union of Angiology, 2014
Carotid intima-media thickness (cIMT) measurements during clinical trials need to have a fixed re... more Carotid intima-media thickness (cIMT) measurements during clinical trials need to have a fixed reference point (also called as bulb edge points) in the anatomy from which the cIMT can be measured. Identification of the bulb edge points in carotid ultrasound images faces the challenge to be detected automatically due to low image quality and variations in ultrasound images, motion artefacts, image acquisition protocols, position of the patient, and orientation of the linear probe with respect to bulb and ultrasound gain controls during acquisition. This paper presents a patented comprehensive methodology for carotid bulb localization and bulb edge detection as a reference point. The method consists of estimating the lumen-intima borders accurately using classification paradigm. Transition points are located automatically based on curvature characteristics. Further we verify and validate the locations of bulb edge points using combination of several local image processing methods such...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011
We present an enhancement method based on nonlinear diffusion filter and statistical intensity ap... more We present an enhancement method based on nonlinear diffusion filter and statistical intensity approaches for smoothing and extracting 3-D vascular system from Magnetic Resonance Angiography (MRA) data. Our method distinguishes and enhances the vessels from the other embedded tissues. The Expectation Maximization (EM) technique is employed with non-linear diffusion in order to find the optimal contrast for enhancing vessels; therefore, smoothing while dimming the embedded tissues around the vessels and brightening the vessels. The non-linear diffusion filter smooths the homogeneous regions while preserving edges. The EM technique finds the optimal statistical parameters based on the probability distribution of the classes to discriminate the tissues in the image. Our enhancement technique has been applied to 4 3-D MRA-TOF datasets consisting of around 300 images and has been compared to the regularized Perona and Malik filter. Our experimental results show that the proposed method e...
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2009
We propose a novel parametric deformable model controlled by shape and visual appearance priors l... more We propose a novel parametric deformable model controlled by shape and visual appearance priors learned from a training subset of co-aligned medical images of goal objects. The shape prior is derived from a linear combination of vectors of distances between the training boundaries and their common centroid. The appearance prior considers gray levels within each training boundary as a sample of a Markov-Gibbs random field with pairwise interaction. Spatially homogeneous interaction geometry and Gibbs potentials are analytically estimated from the training data. To accurately separate a goal object from an arbitrary background, empirical marginal gray level distributions inside and outside of the boundary are modeled with adaptive linear combinations of discrete Gaussians (LCDG). Due to the analytical shape and appearance priors and a simple Expectation-Maximization procedure for getting the object and background LCDG, our segmentation is considerably faster than with most of the know...
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2007
Acute rejection is the most common reason of graft failure after kidney transplantation and early... more Acute rejection is the most common reason of graft failure after kidney transplantation and early detection is crucial to survive the transplanted kidney function. In this paper, we introduce a new approach for the automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures. In the second step, new motion correction models are employed to account for both the global and local motion of the kidney due to patient moving and breathing. Finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the kidney and used in the classification of normal and acute rejection transplants.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2005
Accurate automatic extraction of a 3D cerebrovascular system from images obtained by time-of-flig... more Accurate automatic extraction of a 3D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to small size objects of interest (blood vessels) in each 2D MRA slice and complex surrounding anatomical structures, e.g. fat, bones, or grey and white brain matter. We show that due to a multi-modal nature of MRA data blood vessels can be accurately separated from background in each slice by a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, and we modify the conventional Expectation-Maximization (EM) algorithm to deal with the LCDG. To validate the accuracy of our algorithm, a special 3D geometrical phantom motivated by statistical analysis of t...
Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, 2011
In this chapter, a novel approach to align an image of a textured object with a given prototype w... more In this chapter, a novel approach to align an image of a textured object with a given prototype will be proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov���Gibbs random field (MGRF) with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed ...
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2003
... and enhancement [I]. Such models are traditionally low level stochastic models of the limited... more ... and enhancement [I]. Such models are traditionally low level stochastic models of the limited complexity. ... 191 CA Bouman and B. Liu, “Multiple resolution segmentation of textured images”, IEEE, Trans. ... “Parameter estimation in Gibbs-Markov image models,'' 61h international ...
2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007
Abstract Autism is a developmental disorder characterized by social deficits, impaired communicat... more Abstract Autism is a developmental disorder characterized by social deficits, impaired communication, and restricted and repetitive patterns of behavior. Recent neuropathological studies of autism have revealed abnormal anatomy of the cerebral white matter (CWM) in autistic brains. In this paper we introduced a novel approach to classify autistic from normal subjects based on a new shape analysis of cerebral white matter gyrifications for both normal and autistic subjects. The proposed shape analysis technique consists of three ...
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008
A new approach to align an image of a medical object with a given prototype (reference object) is... more A new approach to align an image of a medical object with a given prototype (reference object) is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a new variant rotation and scaling Markov-Gibbs random field with pairwise interaction model. Similarity to the prototype (reference object) is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype (reference object) using our previous Linear Combination of Discrete Gaussians (LCDG) probabilistic model. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.
Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, 2011
Objects of specific shapes in an image are typically segmented with a deformable model being zero... more Objects of specific shapes in an image are typically segmented with a deformable model being zero level of a geometric-level set function specifying sign-alternate shortest distances to the object boundary from each pixel. The goal shapes are approximated by a linear combination of such 2D distance maps built for mutually aligned images of given training objects. Unfortunately, the approximate shapes may deviate much from the training ones because the space of the distance maps is not closed with respect to linear operations ...
2007 IEEE International Conference on Image Processing, 2007
We propose novel Expectation Maximization (EM) based algorithms for accurate approximation of an ... more We propose novel Expectation Maximization (EM) based algorithms for accurate approximation of an empirical probability distribution of discrete scalar data. The algorithms refine our previous ones in that they approximate the empirical distribution with a linear combination of discrete Gaussians (LCDG). The use of the DGs results in closer approximation and considerably better convergence to a local likelihood maximum compared to previously involved conventional continuous Gaussian densities. Experiments in segmenting multimodal medical images show the proposed algorithms produce more adequate region borders.
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008
A new approach to align an image of a textured object with a given prototype (learned reference o... more A new approach to align an image of a textured object with a given prototype (learned reference object) is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype (learned reference object) is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. To get accurate appearance model, we developed a new approach to automatically select the most important cliques (neighborhood system) that describe the visual appearance of a texture object. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008
We propose a novel parametric deformable model controlled by shape and visual appearance priors l... more We propose a novel parametric deformable model controlled by shape and visual appearance priors learned from a training subset of co-aligned images of goal objects. The shape prior is derived from a linear combination of vectors of distances between the training boundaries and their common centroid. The appearance prior considers gray levels within each training boundary as a sample of a Markov-Gibbs random field with pairwise interaction. Spatially homogeneous interaction geometry and Gibbs potentials are analytically estimated from the training data. To accurately separate a goal object from an arbitrary background, empirical marginal gray level distributions inside and outside of the boundary are modeled with adaptive linear combinations of discrete Gaussians (LCDG). The evolution of the parametric deformable model is based on solving an Eikonal partial differential equation with a new speed function which combines the prior shape, prior appearance, and current appearance models. Due to the analytical shape and appearance priors and a simple Expectation-Maximization procedure for getting the object and background LCDG, our segmentation is considerably faster than most of the known geometric and parametric models. Experiments with various goal images confirm the robustness, accuracy, and speed of our approach.
2008 19th International Conference on Pattern Recognition, 2008
The importance of accurate early diagnostics of dyslexia that severely affects the learning abili... more The importance of accurate early diagnostics of dyslexia that severely affects the learning abilities of children cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We explore a possibility of distinguishing between dyslexic and normal (control) brains by a quantitative shape analysis of CWM gyrifications on 3D Magnetic Resonance (MR) images. Our approach consists of (i) segmentation of the CWM on a 3D brain image using a deformable 3D boundary; (ii) extraction of gyrifications from the segmented CWM, and (iii) shape analysis to quantify thickness of the extracted gyrifications and classify dyslexic and normal subjects. The boundary evolution is controlled by two probabilistic models of visual appearance of 3D CWM: the learned prior and the current appearance model. Initial experimental results suggest that the proposed 3D texture analysis is a promising supplement to the current techniques for diagnosing dyslexia.
Procedings of the British Machine Vision Conference 2007, 2007
A new approach to align an image of a textured object with a given prototype is proposed. Visual ... more A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex 2D/3D objects better than popular conventional algorithms.
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
We propose a modified Expectation-Maximization algorithm that approximates an empirical probabili... more We propose a modified Expectation-Maximization algorithm that approximates an empirical probability density function of scalar data with a linear combination of Gaussians (LCG). Due to both positive and negative components, the LCG approximates inter-class transitions more accurately than a conventional mixture of only positive Gaussians. Experiments in segmenting multi-modal medical images show the proposed LCG-approximation results in more adequate region borders.
Abstract. A new approach to align an image of a textured object with a given prototype under its ... more Abstract. A new approach to align an image of a textured object with a given prototype under its monotone photometric and affine geometric transformations is experimentally compared to more conventional registration algorithms. The approach is based on measuring similarity between the image and prototype by Gibbs energy of characteristic pairwise co-occurrences of the equalized image signals. After an initial alignment, the affine transformation maximizing the energy is found by gradient search. Experiments confirm that our ...
Abstract. We present a fast algorithm for automatic extraction of a 3D cerebrovascular system fro... more Abstract. We present a fast algorithm for automatic extraction of a 3D cerebrovascular system from time-of-flight (TOF) magnetic resonance angiography (MRA) data. Blood vessels are separated from background tissues (fat, bones, or grey and white brain matter) by voxel-wise clas- ...
Abstract. Deformable or active contour, and surface models are power-ful image segmentation techn... more Abstract. Deformable or active contour, and surface models are power-ful image segmentation techniques. We introduce a novel fast and robust bi-directional parametric deformable model which is able to segment re-gions of intricate shape in multi-modal greyscale images. ...
International angiology : a journal of the International Union of Angiology, 2014
Carotid intima-media thickness (cIMT) measurements during clinical trials need to have a fixed re... more Carotid intima-media thickness (cIMT) measurements during clinical trials need to have a fixed reference point (also called as bulb edge points) in the anatomy from which the cIMT can be measured. Identification of the bulb edge points in carotid ultrasound images faces the challenge to be detected automatically due to low image quality and variations in ultrasound images, motion artefacts, image acquisition protocols, position of the patient, and orientation of the linear probe with respect to bulb and ultrasound gain controls during acquisition. This paper presents a patented comprehensive methodology for carotid bulb localization and bulb edge detection as a reference point. The method consists of estimating the lumen-intima borders accurately using classification paradigm. Transition points are located automatically based on curvature characteristics. Further we verify and validate the locations of bulb edge points using combination of several local image processing methods such...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011
We present an enhancement method based on nonlinear diffusion filter and statistical intensity ap... more We present an enhancement method based on nonlinear diffusion filter and statistical intensity approaches for smoothing and extracting 3-D vascular system from Magnetic Resonance Angiography (MRA) data. Our method distinguishes and enhances the vessels from the other embedded tissues. The Expectation Maximization (EM) technique is employed with non-linear diffusion in order to find the optimal contrast for enhancing vessels; therefore, smoothing while dimming the embedded tissues around the vessels and brightening the vessels. The non-linear diffusion filter smooths the homogeneous regions while preserving edges. The EM technique finds the optimal statistical parameters based on the probability distribution of the classes to discriminate the tissues in the image. Our enhancement technique has been applied to 4 3-D MRA-TOF datasets consisting of around 300 images and has been compared to the regularized Perona and Malik filter. Our experimental results show that the proposed method e...
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2009
We propose a novel parametric deformable model controlled by shape and visual appearance priors l... more We propose a novel parametric deformable model controlled by shape and visual appearance priors learned from a training subset of co-aligned medical images of goal objects. The shape prior is derived from a linear combination of vectors of distances between the training boundaries and their common centroid. The appearance prior considers gray levels within each training boundary as a sample of a Markov-Gibbs random field with pairwise interaction. Spatially homogeneous interaction geometry and Gibbs potentials are analytically estimated from the training data. To accurately separate a goal object from an arbitrary background, empirical marginal gray level distributions inside and outside of the boundary are modeled with adaptive linear combinations of discrete Gaussians (LCDG). Due to the analytical shape and appearance priors and a simple Expectation-Maximization procedure for getting the object and background LCDG, our segmentation is considerably faster than with most of the know...
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2007
Acute rejection is the most common reason of graft failure after kidney transplantation and early... more Acute rejection is the most common reason of graft failure after kidney transplantation and early detection is crucial to survive the transplanted kidney function. In this paper, we introduce a new approach for the automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures. In the second step, new motion correction models are employed to account for both the global and local motion of the kidney due to patient moving and breathing. Finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the kidney and used in the classification of normal and acute rejection transplants.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2005
Accurate automatic extraction of a 3D cerebrovascular system from images obtained by time-of-flig... more Accurate automatic extraction of a 3D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to small size objects of interest (blood vessels) in each 2D MRA slice and complex surrounding anatomical structures, e.g. fat, bones, or grey and white brain matter. We show that due to a multi-modal nature of MRA data blood vessels can be accurately separated from background in each slice by a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, and we modify the conventional Expectation-Maximization (EM) algorithm to deal with the LCDG. To validate the accuracy of our algorithm, a special 3D geometrical phantom motivated by statistical analysis of t...
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