Future of Image Registration

Suri, Jasjit S.; Fei, Baowei; Wilson, David L.; Laxminarayan, Swamy; Lo, Chi-Hsiang; Guo, Yubing; Lu, Cheng‐Chang; Tung, Chi-hua

doi:10.1007/0-306-48608-3_13

Cited by 11 publications

(14 citation statements)

References 18 publications

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“…These methods are less efficient than direct methods. Examples of these procedures include region-based algorithms [21,42] and registration based segmentation [35].…”

Section: The Edge Detection Problemmentioning

confidence: 99%

Edge Detection by Adaptive Splitting II. The Three-Dimensional Case

Llanas

Lantarón

2011

J Sci Comput

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“…These methods are less efficient than direct methods. Examples of these procedures include region-based algorithms [21,42] and registration based segmentation [35].…”

Section: The Edge Detection Problemmentioning

confidence: 99%

Edge Detection by Adaptive Splitting II. The Three-Dimensional Case

Llanas

Lantarón

2011

J Sci Comput

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“…'s 22 & 24 are equivalent when = 0. The neighbourhood system (NS) paradigm has been widely used in image analysis [93], [94], [95], [96]. Neighbourhood systems were introduced by Sierpenski and Krieger during the mid-1950s [86], adopted by T.Y.…”

Section: A Tolerance Spaces and Neighbourhood Systemsmentioning

confidence: 99%

Rough Sets and Near Sets in Medical Imaging: A Review

Hassanien¹,

Abraham²,

Peters³

et al. 2009

IEEE Trans. Inform. Technol. Biomed.

148

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Abstract-This paper presents a review of the current literature on rough set and near set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

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“…The GAC term was implemented for regularization and the level set formulation was used for surface evolution. The level set method starts from a seed location or an initial shape and evolves to a discrete set of labeled voxels according to the image information such as image gradient and internal constraints (e.g., smoothness of the resulting segmented surface) [22,23]. Strzelecki et al analyzed level set segmentation methods in simulated vessel structures with differing noise levels and concluded that segmentation results are good even for noisy images [24].…”

Section: Introductionmentioning

confidence: 99%

Vessel segmentation in MRI using a variational image subtraction approach

Saran¹,

Nar²,

Saran³

2014

Turk J Elec Eng & Comp Sci

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Vessel segmentation is important for many clinical applications, such as the diagnosis of vascular diseases, the planning of surgery, or the monitoring of the progress of disease. Although various approaches have been proposed to segment vessel structures from 3-dimensional medical images, to the best of our knowledge, there has been no known technique that uses magnetic resonance imaging (MRI) as prior information within the vessel segmentation of magnetic resonance angiography (MRA) or magnetic resonance venography (MRV) images. In this study, we propose a novel method that uses MRI images as an atlas, assuming that the patient has an MRI image in addition to MRA/MRV images. The proposed approach intends to increase vessel segmentation accuracy by using the available MRI image as prior information. We use a rigid mutual information registration of the MRA/MRV to the MRI, which provides subvoxel accurate multimodal image registration. On the other hand, vessel segmentation methods tend to mostly suffer from imaging artifacts, such as Rician noise, radio frequency (RF) inhomogeneity, or partial volume effects that are generated by imaging devices. Therefore, this proposed method aims to extract all of the vascular structures from MRA/MRI or MRV/MRI pairs at the same time, while minimizing the combined effects of noise and RF inhomogeneity. Our method is validated both quantitatively and visually using BrainWeb phantom images and clinical MRI, MRA, and MRV images.Comparison and observer studies are also realized using the BrainWeb database and clinical images. The computation time is markedly reduced by developing a parallel implementation using the Nvidia compute unified device architecture and OpenMP frameworks in order to allow the use of the method in clinical settings.

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Future of Image Registration

Cited by 11 publications

References 18 publications

Edge Detection by Adaptive Splitting II. The Three-Dimensional Case

Edge Detection by Adaptive Splitting II. The Three-Dimensional Case

Rough Sets and Near Sets in Medical Imaging: A Review

Vessel segmentation in MRI using a variational image subtraction approach

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