Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

744 results found

Luong DVN, Parpas P, Rueckert D, Rustem Bet 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

Conference paper

Wolz R, Chu C, Misawa K, Mori K, Rueckert Det 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

Conference paper

Bhatia KK, Rao A, Price AN, Wolz R, Hajnal J, Rueckert Det 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

Conference paper

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

Conference paper

Cardoso MJ, Wolz R, Modat M, Fox NC, Rueckert D, Ourselin Set 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

Conference paper

Cardoso MJ, Wolz R, Modat M, Fox NC, Rueckert D, Ourselin Set 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.

Conference paper

Shi W, Zhuang X, Pizarro L, Bai W, Wang H, Tung KP, Edwards P, Rueckert Det 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.

Conference paper

Vialard F-X, Rissier L, Cotter CJ, Rueckert Det 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.

Journal article

Aljabar P, Wolz R, Srinivasan L, Counsell SJ, Rutherford MA, Edwards AD, Hajnal JV, Rueckert Det 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

Journal article

Cox D, Shi W, Groves AM, Price AN, Durighel G, Broadhouse KM, Finnemore AE, Edwards AD, Rueckert Det al., 2011, CO-REGISTRATION OF CARDIAC MAGNETIC RESONANCE (CMR) IMAGES FROM PRETERM INFANTS: PILOT WORK IN CREATING A NOVEL NEONATAL CARDIAC ATLAS

Poster

Wolz R, Julkunen V, Koikkalainen J, Niskanen E, Zhang DP, Rueckert D, Soininen H, Lotjonen Jet al., 2011, Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease, PLOS ONE, Vol: 6, ISSN: 1932-6203

Journal article

Risser L, Vialard F-X, Wolz R, Murgasova M, Holm DD, Rueckert Det 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

Journal article

Clerx L, Visser P, Van Rossum I, Scheltens P, Van der Pol L, Barkhof F, Burns L, Knol D, Verhey F, Lapuerta P, Van Schijndel R, De Jong R, Wolz R, Rueckert D, Aalten Pet al., 2011, Comparison of measurements of medial temporal lobe atrophy in the prediction of AD in subjects with MCI

Poster

Sandbach G, Zafeiriou S, Pantic M, Rueckert Det 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.

Conference paper

Heckemann RA, Keihaninejad S, Aljabar P, Gray KR, Nielsen C, Rueckert D, Hajnal JV, Hammers Aet al., 2011, Automatic morphometry in Alzheimer's disease and mild cognitive impairment, NEUROIMAGE, Vol: 56, Pages: 2024-2037, ISSN: 1053-8119

Journal article

Malcolme-Lawes L, Karim R, Juli C, Lim PB, Salukhe TV, Davies DW, Rueckert D, Peters NS, Kanagaratnam Pet al., 2011, AUTOMATED ANALYSIS OF ATRIAL ABLATION-SCAR USING DELAYED-ENHANCED CARDIAC MRI, Annual Conference of the British-Cardiovascular-Society (BCS)

Poster

Koikkalainen J, Lotjonen J, Thurfjell L, Rueckert D, Waldemar G, Soininen Het al., 2011, Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease, NEUROIMAGE, Vol: 56, Pages: 1134-1144, ISSN: 1053-8119

Journal article

Lotjonen J, Wolz R, Koikkalainen J, Julkunen V, Thurfjell L, Lundqvist R, Waldemar G, Soininen H, Rueckert Det al., 2011, Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer's disease, NEUROIMAGE, Vol: 56, Pages: 185-196, ISSN: 1053-8119

Journal article

Kuklisova-Murgasova M, Aljabar P, Srinivasan L, Counsell SJ, Doria V, Serag A, Gousias IS, Boardman JP, Rutherford MA, Edwards AD, Hajnal JV, Rueckert Det al., 2011, A dynamic 4D probabilistic atlas of the developing brain, NEUROIMAGE, Vol: 54, Pages: 2750-2763, ISSN: 1053-8119

Journal article

Deligiannia F, Robinson E, Beckmann CF, Sharp D, Edwards AD, Rueckert Det al., 2011, INFERENCE OF FUNCTIONAL CONNECTIVITY FROM DIRECT AND INDIRECT STRUCTURAL BRAIN CONNECTIONS, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 849-852, ISSN: 1945-7928

Conference paper

Serag A, Aljabar P, Counsell S, Boardman J, Hajnal JV, Rueckert Det al., 2011, CONSTRUCTION OF A 4D ATLAS OF THE DEVELOPING BRAIN USING NON-RIGID REGISTRATION, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 1532-1535, ISSN: 1945-7928

Conference paper

Wolz R, Aljabar P, Hajnal JV, Lotjonen J, Rueckert Det al., 2011, MANIFOLD LEARNING COMBINING IMAGING WITH NON-IMAGING INFORMATION, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 1637-1640, ISSN: 1945-7928

Conference paper

Pizarro L, Delpiano J, Aljabar P, Ruiz-del-Solar J, Rueckert Det al., 2011, TOWARDS DENSE MOTION ESTIMATION IN LIGHT AND ELECTRON MICROSCOPY, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 1939-1942, ISSN: 1945-7928

Conference paper

Serag A, Aljabar P, Counsell S, Boardman J, Hajnal JV, Rueckert Det al., 2011, TRACKING DEVELOPMENTAL CHANGES IN SUBCORTICAL STRUCTURES OF THE PRETERM BRAIN USING MULTI-MODAL MRI, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 349-352, ISSN: 1945-7928

Conference paper

Tung K-P, Shi W-Z, De Silva R, Edwards E, Rueckert Det al., 2011, AUTOMATICAL VESSEL WALL DETECTION IN INTRAVASCULAR CORONARY OCT, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 610-613, ISSN: 1945-7928

Conference paper

Gray KR, Wolz R, Keihaninejad S, Heckemann RA, Aljabar P, Hammers A, Rueckert Det al., 2011, REGIONAL ANALYSIS OF FDG-PET FOR USE IN THE CLASSIFICATION OF ALZHEIMER'S DISEASE, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 1082-1085, ISSN: 1945-7928

Conference paper

Deligianni F, Robinson E, Sharp D, Edwards AD, Rueckert D, Alexander DCet al., 2011, EXPLOITING HIERARCHY IN STRUCTURAL BRAIN NETWORKS, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 871-874, ISSN: 1945-7928

Conference paper

Shi W, Zhuang X, Wang H, Duckett S, Oregan D, Edwards P, Ourselin S, Rueckert Det al., 2011, Automatic Segmentation of Different Pathologies from Cardiac Cine MRI Using Registration and Multiple Component EM Estimation, 6th International Conference on Functional Imaging and Modeling of the Heart (FIMH), Publisher: SPRINGER-VERLAG BERLIN, Pages: 163-170, ISSN: 0302-9743

Conference paper

Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert Det al., 2011, Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia, 2nd International Workshop on Machine Learning in Medical Imaging (MLMI 2011), Publisher: SPRINGER-VERLAG BERLIN, Pages: 159-+, ISSN: 0302-9743

Conference paper

Heckemann RA, Keihaninejad S, Aljabar P, Gray KR, Nielsen C, Rueckert D, Hajnal JV, Hammers Aet al., 2011, A REPOSITORY OF MR MORPHOMETRY DATA IN ALZHEIMER'S DISEASE AND MILD COGNITIVE IMPAIRMENT, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 875-878, ISSN: 1945-7928

Conference paper

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