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161
Mutualinformationbased registration of medical images: a survey
 IEEE TRANSCATIONS ON MEDICAL IMAGING
, 2003
"... An overview is presented of the medical image processing literature on mutualinformationbased registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific ..."
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Cited by 302 (3 self)
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An overview is presented of the medical image processing literature on mutualinformationbased registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutualinformationbased registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.
N.: A LogEuclidean framework for statistics on diffeomorphisms
 In: Proc. MICCAI’06. (2006) 924–931
"... Abstract. In this article, we focus on the computation of statistics of invertible geometrical deformations (i.e., diffeomorphisms), based on the generalization to this type of data of the notion of principal logarithm. Remarkably, this logarithm is a simple 3D vector field, and is welldefined for ..."
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Cited by 100 (43 self)
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Abstract. In this article, we focus on the computation of statistics of invertible geometrical deformations (i.e., diffeomorphisms), based on the generalization to this type of data of the notion of principal logarithm. Remarkably, this logarithm is a simple 3D vector field, and is welldefined for diffeomorphisms close enough to the identity. This allows to perform vectorial statistics on diffeomorphisms, while preserving the invertibility constraint, contrary to Euclidean statistics on displacement fields. We also present here two efficient algorithms to compute logarithms of diffeomorphisms and exponentials of vector fields, whose accuracy is studied on synthetic data. Finally, we apply these tools to compute the mean of a set of diffeomorphisms, in the context of a registration experiment between an atlas an a database of 9 T1 MR images of the human brain. 1
3D Statistical Shape Models Using Direct Optimisation of Description Length
, 2002
"... We describea n a26`('9b method for buildingoptima 3D sta22j9b'2 sha e models from sets oftraj'Hj sha es. Althoughsha e models showconsideraj promisea a bami for segmentingan interpreting imainga ma jordra wba k of theae9`2j h is the need toestaH69 a dense correspondenceadenc a tran ..."
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Cited by 77 (7 self)
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We describea n a26`('9b method for buildingoptima 3D sta22j9b'2 sha e models from sets oftraj'Hj sha es. Althoughsha e models showconsideraj promisea a bami for segmentingan interpreting imainga ma jordra wba k of theae9`2j h is the need toestaH69 a dense correspondenceadenc a trance9 set ofexa')( sha es. It is importa t to esta)`9b the correct correspondence, otherwise poor models ca result. In 2D, thisca be a hieved usingma ua `la9`'H`9b but in 3D this becomesimpra2`269 We show it is possible toesta6jH9 correspondences automatically, byca6)22 the correspondence problema one of finding the`optima) paima)9b`2'2)9 of ea hsha e in thetra'22 set. We describea n explicit representares ofsurfa6 paa6(9b`j"`9a tha ensures the resulting correspondencesad legac ag show how this representaen9ca bemaH('9b2)j to minimise thed933J292 length of the tra'H22 set using the model. This results incompaH models with good generab2('H9 properties. Resultsas reported for two sets ofbiomedica sha es, showingsignifica t improvement in model propertiescompa9' to thoseobta9j) usinga uniform surfam paam92))559b2'6 1
Automatic Construction of Active Appearance Models as an Image Coding Problem
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2004
"... The automatic construction of Active Appearance Models (AAMs) is usually posed as finding the location of the base mesh vertices in the input training images. In this paper, we repose the problem as an energyminimizing image coding problem and propose an efficient gradientdescent algorithm to s ..."
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Cited by 69 (3 self)
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The automatic construction of Active Appearance Models (AAMs) is usually posed as finding the location of the base mesh vertices in the input training images. In this paper, we repose the problem as an energyminimizing image coding problem and propose an efficient gradientdescent algorithm to solve it.
Evaluation of 3D Correspondence Methods for Model Building
 Information Processing in Medical Imaging (IPMI
, 2003
"... The correspondence problem is of high relevance in the construction and use of statistical models. Statistical models are used for a variety of medical application, e.g. segmentation, registration and shape analysis. In this paper, we present comparative studies in three anatomical structures of ..."
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Cited by 64 (11 self)
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The correspondence problem is of high relevance in the construction and use of statistical models. Statistical models are used for a variety of medical application, e.g. segmentation, registration and shape analysis. In this paper, we present comparative studies in three anatomical structures of four di#erent correspondence establishing methods.
Iconic Feature Based Nonrigid Registration: The PASHA Algorithm
, 2004
"... In this paper, we first propose a new subdivision of the image information axis uis for the classification of nonrigid registration algorithms. Namely, we introdu) the notion of iconic featuy based (IFB) algorithms, which lie between geometrical and standard intensitybased algorithms fortheyuM b ..."
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Cited by 62 (20 self)
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In this paper, we first propose a new subdivision of the image information axis uis for the classification of nonrigid registration algorithms. Namely, we introdu) the notion of iconic featuy based (IFB) algorithms, which lie between geometrical and standard intensitybased algorithms fortheyuM both anintensitysimilaritymeasu and a geometrical distance. Then we present a new registration energyfor IFB registration that generalizes some of the existing techniquML We compareou algorithm with other registration approaches, and show the advantages of this energy. Besides, we also present a fasttechniqu for thecompukUy) of local statistics between images, which tuchou to beuyUM on pairs of images having a complex, nonstationaryrelationship between their intensities, as well as an hybridreguSkqy)qL scheme mixing elastic and fluy components. The potential of the algorithm is finallydemonstrated on a clinical application, namelydeep brainstimuMUqy of a Parkinsonian patient. Registration of pre and immediate postoperative MR images allow toquMSy)WS range of the deformationdu topneuU3y)W3Mflover the entire brain,thu yielding tomeasuMy)W3 of the deformation aroun the preoperatively computed stereotactic targets.
Consistent groupwise nonrigid registration for atlas construction
 Proceedings of the IEEE Symposium on Biomedical Imaging (ISBI
, 2004
"... This paper describes a groupwise, nonrigid registration algorithm to simultaneously register all subjects in a population to a common reference (or natural) coordinate system, which is defined to be the average of the population. This natural coordinate system is calculated implicitly by constraini ..."
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Cited by 48 (0 self)
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This paper describes a groupwise, nonrigid registration algorithm to simultaneously register all subjects in a population to a common reference (or natural) coordinate system, which is defined to be the average of the population. This natural coordinate system is calculated implicitly by constraining the sum of all deformations from itself to each subject to be zero. To do this, the gradient projection method for constrained optimization is applied to maximize the similarity between the images, subject to the constraint being satisfied. The algorithm has been tested on a group of 19 brain MR images acquired from a population of subjects with schizophrenia. 1.
Riemannian elasticity: A statistical regularization framework for nonlinear registration
 in Proceedings of the 8th Int. Conf. on Medical Image Computing and ComputerAssisted Intervention  MICCAI 2005, Part II
"... Abstract. In intersubject registration, one often lacks a good model of the transformation variability to choose the optimal regularization. Some works attempt to model the variability in a statistical way, but the reintroduction in a registration algorithm is not easy. In this paper, we interpret ..."
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Cited by 43 (14 self)
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Abstract. In intersubject registration, one often lacks a good model of the transformation variability to choose the optimal regularization. Some works attempt to model the variability in a statistical way, but the reintroduction in a registration algorithm is not easy. In this paper, we interpret the elastic energy as the distance of the GreenSt Venant strain tensor to the identity, which reflects the deviation of the local deformation from a rigid transformation. By changing the Euclidean metric for a more suitable Riemannian one, we define a consistent statistical framework to quantify the amount of deformation. In particular, the mean and the covariance matrix of the strain tensor can be consistently and efficiently computed from a population of nonlinear transformations. These statistics are then used as parameters in a Mahalanobis distance to measure the statistical deviation from the observed variability, giving a new regularization criterion that we called the statistical Riemannian elasticity. This new criterion is able to handle anisotropic deformations and is inverseconsistent. Preliminary results show that it can be quite easily implemented in a nonrigid registration algorithms. 1
Groupwise construction of appearance models using piecewise affine deformations
 in Proceedings of 16 th British Machine Vision Conference
, 2005
"... We describe an algorithm for obtaining correspondences across a group of images of deformable objects. The approach is to construct a statistical model of appearance which can encode the training images as compactly as possible (a Minimum Description Length framework). Correspondences are defined by ..."
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Cited by 42 (15 self)
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We describe an algorithm for obtaining correspondences across a group of images of deformable objects. The approach is to construct a statistical model of appearance which can encode the training images as compactly as possible (a Minimum Description Length framework). Correspondences are defined by piecewise linear interpolation between a set of control points defined on each image. Given such points a model can be constructed, which can approximate every image in the set. The description length encodes the cost of the model, the parameters and most importantly, the residuals not explained by the model. By modifying the positions of the control points we can optimise the description length, leading to good correspondence. We describe the algorithm in detail and give examples of its application to MR brain images and to faces. We also describe experiments which use a recentlyintroduced specificity measure to evaluate the performance of different components of the algorithm. 1
Deformable medical image registration: A survey
 IEEE TRANSACTIONS ON MEDICAL IMAGING
, 2013
"... Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multimodality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudin ..."
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Cited by 35 (1 self)
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Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multimodality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.