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The Dual Bootstrap Iterative Closest Point Algorithm with Application to Retinal Image Registration. (2003)

by C Stewart, C Tsai, B Roysam
Venue:IEEE Trans. Med. Imaging
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Image Change Detection Algorithms: A Systematic Survey

by Richard J. Radke, Srinivas Andra, Omar Al-Kofahi, Badrinath Roysam - IEEE Transactions on Image Processing , 2005
"... Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. T ..."
Abstract - Cited by 236 (3 self) - Add to MetaCart
Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.
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...dratic model suffices. Several modern registration algorithms are capable of switching automatically to higher order transformations after being initialized with a low-order similarity transformation =-=[33]-=-. In some scenarios (e.g., when the cameras that produced the images have widely spaced optical centers, or when the scene consists of deformable/articulated objects), a nonglobal transformation may n...

Local adaptivity to variable smoothness for exemplar-based image denoising and representation

by Charles Kervrann, Jérôme Boulanger , 2005
"... ..."
Abstract - Cited by 66 (6 self) - Add to MetaCart
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Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures

by Michal Sofka, Charles V. Stewart - IEEE TMI , 2006
"... Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matchedfilter re ..."
Abstract - Cited by 52 (1 self) - Add to MetaCart
Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matchedfilter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a 6-dimensional measurement vector at each pixel. A training technique is used to develop a mapping of this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the Hessian of intensities show substantial improvements both qualitatively and quantitatively. The Hessian can be used in place of the matched filter to obtain similar but less-substantial improvements or to steer the matched filter by preselecting kernel orientations. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an e#cient and e#ective vessel centerline extraction algorithm.
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...age analysis and processing because vessels are the predominant and most stable structures appearing in the images. Many published algorithms for optic disc detection [12, 19, 65], image registration =-=[5, 41, 45, 52, 59, 69, 71]-=-, change detection [16, 18, 21, 22, 50, 58, 70], pathology detection and quantification [25], tracking in video sequences [33, 43, 55], and computer-aided screening systems [29, 46, 60, 68] depend on ...

Deformable medical image registration: A survey

by Aristeidis Sotiras, Christos Davatzikos, Nikos Paragios - 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) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudin ..."
Abstract - Cited by 35 (1 self) - Add to MetaCart
Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality 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.
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...pproach for specific applications. They are of interest when intensity information is undermined due to the presence of pathologies while geometric structures remain stable (e.g., retina registration =-=[241]-=-). Geometric registration has also important applications in image-guided interventions [242,243]. On the other hand, iconic methods, often referred to as either voxel-based or intensity-based methods...

Registration of Challenging Image Pairs: Initialization, Estimation, and Decision

by Gehua Yang, Charles V. Stewart, Michal Sofka, Chia-Ling Tsai , 2007
"... Our goal is an automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and p ..."
Abstract - Cited by 28 (4 self) - Add to MetaCart
Our goal is an automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and physical changes in the scene. An important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well. We propose a complete algorithm including techniques for initialization, for estimating transformation parameters, and for automatically deciding if an estimate is correct. Keypoints extracted and matched between images are used to generate initial similarity transform estimates, each accurate over a small region. These initial estimates are rank-ordered and tested individually in succession. Each estimate is refined using the Dual-Bootstrap ICP algorithm, driven by matching of multiscale features. A three-part decision criteria, combining measurements of alignment accuracy, stability in the estimate, and consistency in the constraints, determines whether the refined transformation estimate is accepted as correct. Experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99.8 percent of the misalignments that occur when all possible pairs are tried. The algorithm substantially out-performs algorithms based on keypoint matching alone.

Efficient Sequential Correspondence Selection by Cosegmentation

by Jan Čech, et al. , 2009
"... In many retrieval, object recognition and wide baseline stereo methods, correspondences of interest points (distinguished regions) are commonly established by matching compact descriptors such as SIFTs. We show that a subsequent cosegmentation process coupled with a quasi-optimal sequential decision ..."
Abstract - Cited by 27 (7 self) - Add to MetaCart
In many retrieval, object recognition and wide baseline stereo methods, correspondences of interest points (distinguished regions) are commonly established by matching compact descriptors such as SIFTs. We show that a subsequent cosegmentation process coupled with a quasi-optimal sequential decision process leads to a correspondence verification procedure that (i) has high precision (is highly discriminative) (ii) has good recall and (iii) is fast. The sequential decision on the correctness of a correspondence is based on simple statistics of a modified dense stereo matching algorithm. The statistics are projected on a prominent discriminative direction by SVM. Wald’s sequential probability ratio test is performed on the SVM projection computed on progressively larger cosegmented regions. We show experimentally that the proposed Sequential Correspondence Verification (SCV) algorithm significantly outperforms the standard correspondence selection method based on SIFT distance ratios on challenging matching problems.
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...mprove the precision of registration. The value of correspondence growing methods has been demonstrated in [19, 4], sometimes with impressive results, e.g. those achieved by the dual bootstrap method =-=[21, 18]-=-. Most approaches to simultaneous cosegmentation and registration focus on the problem of finding the largest corresponding domain and codomain [21, 4, 8]. Our objective is almost opposite: given acce...

Learning-based deformable registration of MR brain images

by Guorong Wu, Feihu Qi, Dinggang Shen - IEEE Transactions on Medical Imaging , 2006
"... Abstract—This paper presents a learning-based method for deformable registration of magnetic resonance (MR) brain images. There are two novelties in the proposed registration method. First, a set of best-scale geometric features are selected for each point in the brain, in order to facilitate corres ..."
Abstract - Cited by 22 (8 self) - Add to MetaCart
Abstract—This paper presents a learning-based method for deformable registration of magnetic resonance (MR) brain images. There are two novelties in the proposed registration method. First, a set of best-scale geometric features are selected for each point in the brain, in order to facilitate correspondence detection during the registration procedure. This is achieved by optimizing an energy function that requires each point to have its best-scale geometric features consistent over the corresponding points in the training samples, and at the same time distinctive from those of nearby points in the neighborhood. Second, the active points used to drive the brain registration are hierarchically selected during the registration procedure, based on their saliency and consistency measures. That is, the image points with salient and consistent features (across different individuals) are considered for the initial registration of two images, while other less salient and consistent points join the registration procedure later. By incorporating these two novel strategies into the framework of the HAMMER registration algorithm, the registration accuracy has been improved according to the results on simulated brain data, and also visible improvement is observed particularly in the cortical regions of real brain data. Index Terms—Best features, best scale selection, consistency measurement, deformable registration, feature-based registration, hierarchical registration, learning-based method, saliency measurement. I.

Robust FFT-Based Scale-Invariant Image Registration with Image Gradients

by Georgios Tzimiropoulos, Tania Stathaki - IEEE Transactions on Pattern Analysis and Machine Intelligence
"... We present a fast and robust gradient-based scale-invariant image registration technique which operates in the frequency domain. The algorithm combines the natural advantages of good feature selection offered by gradient-based methods with the robustness and speed provided by FFT-based correlation s ..."
Abstract - Cited by 19 (6 self) - Add to MetaCart
We present a fast and robust gradient-based scale-invariant image registration technique which operates in the frequency domain. The algorithm combines the natural advantages of good feature selection offered by gradient-based methods with the robustness and speed provided by FFT-based correlation schemes. Experimentation with real images taken from a popular database showed that, unlike any other Fourier-based techniques, the method was able to estimate translations, arbitrary rotations and scale factors up to 6.
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...ted motion parameters, and then superimposed on the zoomed-out image. In general, assuming that the given images share a sufficient number of image features, spatial domain registration schemes [21], =-=[22]-=- are able to handle more challenging registration problems than the proposed method does such as affine distortions and severe partial matching scenarios. However, this is not the case for face regist...

Retinal Vessel Extraction Using Multiscale Matched Filters, Confidence and Edge Measures

by Michal Sofka, Charles V. Stewart , 2005
"... Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matched filter r ..."
Abstract - Cited by 13 (1 self) - Add to MetaCart
Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matched filter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a 6-dimensional measurement vector at each pixel. A learning technique is applied to map this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the intensity Hessian show substantial improvements both qualitatively and quantitatively. When the Hessian is used in place of the matched filter, similar but less-substantial improvements are obtained. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an e#cient and e#ective vessel extraction algorithm.
(Show Context)

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...age analysis and processing because vessels are the predominant and most stable structures appearing in the images. Many published algorithms for optic disc detection [10, 17, 62], image registration =-=[6, 38, 42, 49, 56, 66, 68]-=-, change detection [14, 16, 19, 20, 47, 55, 67], pathology detection and quantification [23], tracking in video sequences [30, 40, 52], and computer-aided screening systems [26, 43, 57, 65] depend on ...

A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration

by Jian Chen, Jie Tian, Noah Lee, Jian Zheng, R. Theodore Smith, Andrew F. Laine , 2009
"... Abstract—Detection of vascular bifurcations is a challenging task in multimodal retinal image registration. Existing algorithms based on bifurcations usually fail in correctly aligning poor quality retinal image pairs. To solve this problem, we propose a novel highly distinctive local feature descri ..."
Abstract - Cited by 10 (0 self) - Add to MetaCart
Abstract—Detection of vascular bifurcations is a challenging task in multimodal retinal image registration. Existing algorithms based on bifurcations usually fail in correctly aligning poor quality retinal image pairs. To solve this problem, we propose a novel highly distinctive local feature descriptor named partial intensity invariant feature descriptor (PIIFD) and describe a robust automatic retinal image registration framework named Harris-PIIFD. PIIFD is invariant to image rotation, partially invariant to image intensity, affine transformation, and viewpoint/perspective change. Our Harris-PIIFD framework consists of four steps. First, corner points are used as control point candidates instead of bifurcations since corner points are sufficient and uniformly distributed across the image domain. Second, PIIFDs are extracted for all corner points, and a bilateral matching technique is applied to identify corresponding PIIFDs matches between image pairs. Third, incorrect
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...umination changes and significant initial-misalignment, suggesting that area-based approaches may be susceptible to occlusion, background changes caused by pathologies, and pose changes of the camera =-=[35]-=-. Compared with area-based registration, feature-based approaches [25]–[41] are more appropriate for retinal image registration. Feature-based approaches typically involve extracting features and sear...

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