700 results found
Karim R, Arujuna A, Brazier A, et al., 2012, Validation of a novel method for the automatic segmentation of left atrial scar from delayed-enhancement magnetic resonance, Pages: 254-262, ISSN: 0302-9743
Delayed-enhancement magnetic resonance imaging is an effective technique for imaging left atrial (LA) scars both pre- and post- radio-frequency ablation for the treatment of atrial fibrillation. Existing techniques for LA scar segmentation require expert manual interaction, making them tedious and prone to high observer variability. In this paper, a novel automatic segmentation algorithm for segmenting LA scar was validated using digital phantoms and clinical data from 11 patients. The performance of the approach was compared to the two leading semi-automatic techniques and the ground truth of manual segmentations by 2 expert observers. The novel approach was shown to be accurate in terms of Dice coefficient, robust to typical image intensity variability, and much faster in terms of execution time. © 2012 Springer-Verlag.
Shi W, Zhuang X, Wolz R, et al., 2012, A multi-image graph cut approach for cardiac image segmentation and uncertainty estimation, Pages: 178-187, ISSN: 0302-9743
Registration and segmentation uncertainty may be important information to convey to a user when automatic image analysis is performed. Uncertainty information may be used to provide additional diagnostic information to traditional analysis of cardiac function. In this paper, we develop a framework for the automatic segmentation of the cardiac anatomy from multiple MR images. We also define the registration and segmentation uncertainty and explore its use for diagnostic purposes. Our framework uses cardiac MR image sequences that are widely available in clinical practice. We improve the performance of the cardiac segmentation algorithms by combining information from multiple MR images using a graph-cut based segmentation. We evaluate this framework on images from 32 subjects: 13 patients with ischemic cardiomyopathy, 14 patients with dilated cardiomyopathy and 5 normal volunteers. Our results indicate that the proposed method is capable of producing segmentation results with very high robustness and high accuracy with minimal user interaction across all subject groups. We also show that registration and segmentation uncertainties are good indicators for segmentation failures as well as good predictors for the functional abnormality of the subject. © 2012 Springer-Verlag.
Gray KR, Wolz R, Heckemann RA, et al., 2012, Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease, NEUROIMAGE, Vol: 60, Pages: 221-229, ISSN: 1053-8119
Vounou M, Janousova E, Wolz R, et al., 2012, Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease, NEUROIMAGE, Vol: 60, Pages: 700-716, ISSN: 1053-8119
van der Lijn F, Verhaaren BFJ, Ikram MA, et al., 2012, Automated measurement of local white matter lesion volume, NEUROIMAGE, Vol: 59, Pages: 3901-3908, ISSN: 1053-8119
Serag A, Aljabar P, Ball G, et al., 2012, Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression, NEUROIMAGE, Vol: 59, Pages: 2255-2265, ISSN: 1053-8119
Luong DVN, Parpas P, Rueckert D, et al., 2012, Solving MRF Minimization by Mirror Descent, 8th International Symposium on Visual Computing (ISVC), Publisher: SPRINGER-VERLAG BERLIN, Pages: 587-598, ISSN: 0302-9743
Pszczolkowski S, Pizarro L, Guerrero R, et al., 2012, Nonrigid Free-Form Registration Using Landmark-Based Statistical Deformation Models, Conference on Medical Imaging - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Bhatia KK, Price AN, Hajnal JV, et al., 2012, Localised manifold learning for cardiac image analysis, Conference on Medical Imaging - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Lotjonen J, Wolz R, Koikkalainen J, et al., 2012, HIPPOCAMPAL ATROPHY RATE USING AN EXPECTATION MAXIMIZATION CLASSIFIER WITH A DISEASE-SPECIFIC PRIOR, 9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 1164-1167
Soininen H, Mattila J, Koikkalainen J, et al., 2012, Software Tool for Improved Prediction of Alzheimer's Disease, 10th International Conference on Alzheimers and Parkinsons Diseases (AD/PD), Publisher: KARGER, Pages: 149-152, ISSN: 1660-2854
Wolz R, Chu C, Misawa K, et al., 2012, Multi-organ Abdominal CT Segmentation Using Hierarchically Weighted Subject-Specific Atlases, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 10-17, ISSN: 0302-9743
Bhatia KK, Rao A, Price AN, et al., 2012, Hierarchical Manifold Learning, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 512-519, ISSN: 0302-9743
Tung KP, Shi WZ, Pizarro L, et al., 2012, Automatic Detection of Coronary Stent Struts in Intravascular OCT imaging, Conference on Medical Imaging - Computer-Aided Diagnosis, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Caballero J, Rueckert D, Hajnal JV, 2012, Dictionary Learning and Time Sparsity in Dynamic MRI, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 256-263, ISSN: 0302-9743
Shi W, Zhuang X, Pizarro L, et al., 2012, Registration using sparse free-form deformations, Pages: 659-666, ISSN: 0302-9743
© Springer-Verlag Berlin Heidelberg 2012. Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.
Guerrero R, Pizarro L, Wolz R, et al., 2012, LANDMARK LOCALISATION IN BRAIN MR IMAGES USING FEATURE POINT DESCRIPTORS BASED ON 3D LOCAL SELF-SIMILARITIES, 9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 1535-1538
Ledig C, Wolz R, Aljabar P, et al., 2012, MULTI-CLASS BRAIN SEGMENTATION USING ATLAS PROPAGATION AND EM-BASED REFINEMENT, 9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 896-899
Serag A, Aljabar P, Counsell S, et al., 2012, LISA: LONGITUDINAL IMAGE REGISTRATION VIA SPATIO-TEMPORAL ATLASES, 9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 334-337
Cardoso MJ, Wolz R, Modat M, et al., 2012, Geodesic information flows, Pages: 262-270, ISSN: 0302-9743
© Springer-Verlag Berlin Heidelberg 2012. Homogenising the availability of manually generated information in large databases has been a key challenge of medical imaging for many years. Due to the time consuming nature of manually segmenting, parcellating and localising landmarks in medical images, these sources of information tend to be scarce and limited to small, and sometimes morphologically similar, subsets of data. In this work we explore a new framework where these sources of information can be propagated to morphologically dissimilar images by diffusing and mapping the information through intermediate steps. The spatially variant data embedding uses the local morphology and intensity similarity between images to diffuse the information only between locally similar images. This framework can thus be used to propagate any information from any group of subject to every other subject in a database with great accuracy. Comparison to state-of-the-art propagation methods showed highly statistically significant (p < 10−4) improvements in accuracy when propagating both structural parcelations and brain segmentations geodesically.
Mattila J, Soininen H, Koikkalainen J, et al., 2012, Optimizing the Diagnosis of Early Alzheimer's Disease in Mild Cognitive Impairment Subjects, JOURNAL OF ALZHEIMERS DISEASE, Vol: 32, Pages: 969-979, ISSN: 1387-2877
Shi W, Zhuang X, Pizarro L, et al., 2012, Registration Using Sparse Free-Form Deformations, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 659-666, ISSN: 0302-9743
Cardoso MJ, Wolz R, Modat M, et al., 2012, Geodesic Information Flows, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 262-270, ISSN: 0302-9743
Vialard F-X, Rissier L, Cotter CJ, et al., 2012, Diffeomorphic 3D image registration via geodesic shooting using an efficient adjoint calculation, International Journal of Computer Vision
In the context of large deformations by diffeomorphisms, we propose a new diffeomorphic registration algorithm for 3D images that performs the optimization directly on the set of geodesic flows. The key contribution of this work is to provide an accurate estimation of the so-called initial momentum, which is a scalar function encoding the optimal deformation between two images through the Hamiltonian equations of geodesics. Since the initial momentum has proven to be a key tool for statistics on shape spaces, our algorithm enables more reliable statistical comparisons for 3D images.Our proposed algorithm is a gradient descent on the initial momentum, where the gradient is calculated using standard methods from optimal control theory. To improve the numerical efficiency of the gradient computation, we have developed an integral formulation of the adjoint equations associated with the geodesic equations.We then apply it successfully to the registration of 2D phantom images and 3D cerebral images. By comparing our algorithm to the standard approach of Beg et al. (Int. J. Comput. Vis. 61:139–157, 2005), we show that it provides a more reliable estimation of the initial momentum for the optimal path. In addition to promising statistical applications, we finally discuss different perspectives opened by this work, in particular in the new field of longitudinal analysis of biomedical images.
Aljabar P, Wolz R, Srinivasan L, et al., 2011, A Combined Manifold Learning Analysis of Shape and Appearance to Characterize Neonatal Brain Development, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 30, Pages: 2072-2086, ISSN: 0278-0062
Cox D, Shi W, Groves AM, et al., 2011, CO-REGISTRATION OF CARDIAC MAGNETIC RESONANCE (CMR) IMAGES FROM PRETERM INFANTS: PILOT WORK IN CREATING A NOVEL NEONATAL CARDIAC ATLAS
Wolz R, Julkunen V, Koikkalainen J, et al., 2011, Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease, PLOS ONE, Vol: 6, ISSN: 1932-6203
Risser L, Vialard F-X, Wolz R, et al., 2011, Simultaneous Multi-scale Registration Using Large Deformation Diffeomorphic Metric Mapping, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 30, Pages: 1746-1759, ISSN: 0278-0062
Clerx L, Visser P, Van Rossum I, et al., 2011, Comparison of measurements of medial temporal lobe atrophy in the prediction of AD in subjects with MCI
Sandbach G, Zafeiriou S, Pantic M, et al., 2011, A dynamic approach to the recognition of 3D facial expressions and their temporal models, Pages: 406-413
In this paper we propose a method that exploits 3D motion-based features between frames of 3D facial geometry sequences for dynamic facial expression recognition. An expressive sequence is modeled to contain an onset followed by an apex and an offset. Feature selection methods are applied in order to extract features for each of the onset and offset segments of the expression. These features are then used to train a Hidden Markov Model in order to model the full temporal dynamics of the expression. The proposed fully automatic system was tested in a subset of the BU-4DFE database for the recognition of happiness, anger and surprise. Comparisons with a similar system based on the motion extracted from facial intensity images was also performed. The attained results suggest that the use of the 3D information does indeed improve the recognition accuracy when compared to the 2D data. © 2011 IEEE.
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