DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting
TL;DR: A general-purpose deformable registration algorithm referred to as "DRAMMS" is presented, which extracts Gabor attributes at each voxel and selects the optimal components, so that they form a highly distinctive morphological signature reflecting the anatomical context around each v oxel in a multi-scale and multi-resolution fashion.
About: This article is published in Medical Image Analysis.The article was published on 2011-08-01 and is currently open access. It has received 420 citations till now. The article focuses on the topics: Image registration & Voxel.
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TL;DR: This review covers computer-assisted analysis of images in the field of medical imaging and introduces the fundamentals of deep learning methods and their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on.
Abstract: This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.
2,653 citations
TL;DR: This paper attempts to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain, and provides an extensive account of registration techniques in a systematic manner.
Abstract: Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) 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.
1,434 citations
Cites background or methods from "DRAMMS: Deformable registration via..."
...[323] optimized the Gabor features to be more distinctive and employed the notion of mutual saliency to let the most reliable points drive the registration process....
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...[323] used it to solve feature-based registration, while Sotiras et al....
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...filters in deformable image registration [323]....
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...Local information may also be incorporated by exploiting the local frequency representations obtained as response to Gabor filters [323], [324]....
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TL;DR: It is shown that the DTI measurements are highly site‐specific, highlighting the need of correcting for site effects before performing downstream statistical analyses, and that ComBat, a popular batch‐effect correction tool used in genomics, performs best at modeling and removing the unwanted inter‐site variability in FA and MD maps.
612 citations
Cites methods from "DRAMMS: Deformable registration via..."
...The FA and MD maps were then non-linearly registered to the Eve template using DRAMMS [Ou et al., 2011]....
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...5) and then non-linearly registered to the Eve template using DRAMMS [Ou et al., 2011] (v1....
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TL;DR: Multi-atlas segmentation (MAS) is becoming one of the most widely used and successful image segmentation techniques in biomedical applications as mentioned in this paper, and it has been widely used in medical image classification.
587 citations
TL;DR: A modality independent neighbourhood descriptor (MIND), based on the concept of image self-similarity, which has been introduced for non-local means filtering for image denoising, is proposed and applied for the registration of clinical 3D thoracic CT scans between inhale and exhale as well as the alignment of 3D CT and MRI scans.
580 citations
Cites background or methods from "DRAMMS: Deformable registration via..."
...…of our approach is that it748 requires an anatomical feature to be present in both modalities,749 if this assumption is violated the concept of mutual-saliency750 (Ou et al. (2011)) could be incorporated to improve the robust-751 ness in these cases.752 Further improvements might be possible....
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...A limitation of our approach is that it 748 requires an anatomical feature to be present in both modalities, 749 if this assumption is violated the concept of mutual-saliency 750 (Ou et al. (2011)) could be incorporated to improve the robust751 ness in these cases....
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References
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TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Abstract: Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
14,509 citations
TL;DR: DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.
6,999 citations
TL;DR: A review of recent as well as classic image registration methods to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas.
6,842 citations
"DRAMMS: Deformable registration via..." refers background in this paper
...Image registration is the process of finding the optimal transformation that aligns different imaging data into spatial correspondence, so that after registration, the same anatomic structures occupy the same spatial locations in different images (Zitova and Flusser, 2003; Crum et al., 2004)....
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TL;DR: The results clearly indicate that the proposed nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
Abstract: In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
5,490 citations
"DRAMMS: Deformable registration via..." refers background in this paper
...Examples of the latter category include (Glocker et al., 2008; Vercauteren et al., 2007, 2009; Ou and Davatzikos, 2009; D’Agostino et al., 2003; Kybic and Unser, 2003; Christensen et al., 1994; Collins et al., 1994; Thirion, 1998; Rueckert et al., 1999; Maes et al., 1997; Wells et al., 1996; Friston et al., 1995), among others....
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01 Jan 1979
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
Abstract: In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives.
5,112 citations