The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Rolf Bendl - One of the best experts on this subject based on the ideXlab platform.
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combination of intensity based Image Registration with 3d simulation in radiation therapy
Physics in Medicine and Biology, 2008Co-Authors: Pan Li, Urban Malsch, Rolf BendlAbstract:Modern techniques of radiotherapy like intensity modulated radiation therapy (IMRT) make it possible to deliver high dose to tumors of different irregular shapes at the same time sparing surrounding healthy tissue. However, internal tumor motion makes precise calculation of the delivered dose distribution challenging. This makes analysis of tumor motion necessary. One way to describe target motion is using Image Registration. Many Registration methods have already been developed previously. However, most of them belong either to geometric approaches or to intensity approaches. Methods which take account of anatomical information and results of intensity matching can greatly improve the results of Image Registration. Based on this idea, a combined method of Image Registration followed by 3D modeling and simulation was introduced in this project. Experiments were carried out for five patients 4DCT lung datasets. In the 3D simulation, models obtained from Images of end-exhalation were deformed to the state of end-inhalation. Diaphragm motions were around −25 mm in the cranial–caudal (CC) direction. To verify the quality of our new method, displacements of landmarks were calculated and compared with measurements in the CT Images. Improvement of accuracy after simulations has been shown compared to the results obtained only by intensity-based Image Registration. The average improvement was 0.97 mm. The average Euclidean error of the combined method was around 3.77 mm. Unrealistic motions such as curl-shaped deformations in the results of Image Registration were corrected. The combined method required less than 30 min. Our method provides information about the deformation of the target volume, which we need for dose optimization and target definition in our planning system.
Jihong Guan - One of the best experts on this subject based on the ideXlab platform.
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a novel Image Registration algorithm for remote sensing under affine transformation
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Zhili Song, Shuigeng Zhou, Jihong GuanAbstract:With the help of the histogram of triangle area representation (TAR) and feature matching strategy, a new effective Image Registration approach for remote sensing is proposed in this paper. This approach is based on a robust transformation parameter estimation algorithm called the histogram of TAR sample consensus (HTSC in short). The HTSC algorithm can replace the existing random sample consensus (RANSAC) and progressive sample consensus (PROSAC) methods that have been widely used in the transformation parameter estimation step of remote-sensing Image Registration, for it can efficiently calculate the consensus set with a higher accuracy. This paper lays down a new way to build a robust transformation parameter estimator based on the invariance constraint for remote-sensing Image Registration. Analogous to the two types of well-known existing transformation parameter estimation methods RANSAC and PROSAC, HTSC can serve as a new type (or the third type if we treat RANSAC and PROSAC as the first and the second types) of such methods, as it adopts the transformation-invariance information to find the consensus.
Min Xu - One of the best experts on this subject based on the ideXlab platform.
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Salient Feature Region: A New Method for Retinal Image Registration
IEEE Transactions on Information Technology in Biomedicine, 2011Co-Authors: Jian Zheng, Jie Tian, Kexin Deng, Xing Zhang, Min XuAbstract:Retinal Image Registration is crucial for the diagnoses and treatments of various eye diseases. A great number of methods have been developed to solve this problem; however, fast and accurate Registration of low-quality retinal Images is still a challenging problem since the low content contrast, large intensity variance as well as deterioration of unhealthy retina caused by various pathologies. This paper provides a new retinal Image Registration method based on salient feature region (SFR). We first propose a well-defined region saliency measure that consists of both local adaptive variance and gradient field entropy to extract the SFRs in each Image. Next, an innovative local feature descriptor that combines gradient field distribution with corresponding geometric information is then computed to match the SFRs accurately. After that, normalized cross-correlation-based local rigid Registration is performed on those matched SFRs to refine the accuracy of local alignment. Finally, the two Images are registered by adopting high-order global transformation model with locally well-aligned region centers as control points. Experimental results show that our method is quite effective for retinal Image Registration.
Marius Staring - One of the best experts on this subject based on the ideXlab platform.
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A deep learning framework for unsupervised affine and deformable Image Registration
Medical image analysis, 2018Co-Authors: Bob D De Vos, Floris F Berendsen, Max A Viergever, Marius Staring, Hessam Sokooti, Ivana IsgumAbstract:Image Registration, the process of aligning two or more Images, is the core technique of many (semi-)automatic medical Image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for Image Registration. Thus far training of ConvNets for Registration was supervised using predefined example Registrations. However, obtaining example Registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for Image Registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable Image Registration. In the DLIR framework ConvNets are trained for Image Registration by exploiting Image similarity analogous to conventional intensity-based Image Registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen Images in one shot. We propose flexible ConvNets designs for affine Image Registration and for deformable Image Registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine Image Registration. We show for Registration of cardiac cine MRI and Registration of chest CT that performance of the DLIR framework is comparable to conventional Image Registration while being several orders of magnitude faster.
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A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Bob D De Vos, Floris F Berendsen, Max A Viergever, Marius Staring, Hessam Sokooti, Ivana IsgumAbstract:Image Registration, the process of aligning two or more Images, is the core technique of many (semi-)automatic medical Image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for Image Registration. Thus far training of ConvNets for Registration was supervised using predefined example Registrations. However, obtaining example Registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for Image Registration, we propose the Deep Learning Image Registration (DLIR) framework for \textit{unsupervised} affine and deformable Image Registration. In the DLIR framework ConvNets are trained for Image Registration by exploiting Image similarity analogous to conventional intensity-based Image Registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen Images in one shot. We propose flexible ConvNets designs for affine Image Registration and for deformable Image Registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine Image Registration. We show for Registration of cardiac cine MRI and Registration of chest CT that performance of the DLIR framework is comparable to conventional Image Registration while being several orders of magnitude faster.
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a survey of medical Image Registration under review
Medical Image Analysis, 2016Co-Authors: Max A Viergever, Marius Staring, J Antoine B Maintz, Stefan Klein, Keelin Murphy, Josien P W PluimAbstract:A retrospective view on the past two decades of the field of medical Image Registration is presented, guided by the article "A survey of medical Image Registration" (Maintz and Viergever, 1998). It shows that the classification of the field introduced in that article is still usable, although some modifications to do justice to advances in the field would be due. The main changes over the last twenty years are the shift from extrinsic to intrinsic Registration, the primacy of intensity-based Registration, the breakthrough of nonlinear Registration, the progress of inter-subject Registration, and the availability of generic Image Registration software packages. Two problems that were called urgent already 20 years ago, are even more urgent nowadays: Validation of Registration methods, and translation of results of Image Registration research to clinical practice. It may be concluded that the field of medical Image Registration has evolved, but still is in need of further development in various aspects.
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elastix a toolbox for intensity based medical Image Registration
IEEE Transactions on Medical Imaging, 2010Co-Authors: Stefa Klei, Marius Staring, Keeli Murphy, Ma A Viergeve, Josien P W PluimAbstract:Medical Image Registration is an important task in medical Image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for Image Registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical Image Registration. The software consists of a collection of algorithms that are commonly used to solve medical Image Registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different Registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different Registration methods is illustrated with three example experiments, in which individual components of the Registration method are varied.
Zhili Song - One of the best experts on this subject based on the ideXlab platform.
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a novel Image Registration algorithm for remote sensing under affine transformation
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Zhili Song, Shuigeng Zhou, Jihong GuanAbstract:With the help of the histogram of triangle area representation (TAR) and feature matching strategy, a new effective Image Registration approach for remote sensing is proposed in this paper. This approach is based on a robust transformation parameter estimation algorithm called the histogram of TAR sample consensus (HTSC in short). The HTSC algorithm can replace the existing random sample consensus (RANSAC) and progressive sample consensus (PROSAC) methods that have been widely used in the transformation parameter estimation step of remote-sensing Image Registration, for it can efficiently calculate the consensus set with a higher accuracy. This paper lays down a new way to build a robust transformation parameter estimator based on the invariance constraint for remote-sensing Image Registration. Analogous to the two types of well-known existing transformation parameter estimation methods RANSAC and PROSAC, HTSC can serve as a new type (or the third type if we treat RANSAC and PROSAC as the first and the second types) of such methods, as it adopts the transformation-invariance information to find the consensus.