Registration of multi-modality medical images by soft computing approach

Hata, Yutaka; Kobashi, Syoji; Hirano, Shoji; Ishikawa, Makato

doi:10.1109/iconip.1999.844653

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Fuzzy Sets manifest the perception of partial membership of an element within the set-this permits Fuzzy sets to tackle uncertainty and inaccuracies. Fuzzy Sets have been explicitly applied to Image registration techniques [79][80]. It has also been utilized to choose and pre-process the extracted features to be registered.…”

Section: B) Fuzzy Setsmentioning

confidence: 99%

Image Registration Techniques: A Survey

Nag¹

2017

Preprint

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Image Registration is the process of aligning two or more images of the same scene with reference to a particular image. The images are captured from various sensors at different times and at multiple view-points. Thus to get a better picture of any change of a scene/object over a considerable period of time image registration is important. Image registration finds application in medical sciences, remote sensing and in computer vision. This paper presents a detailed review of several approaches which are classified accordingly along with their contributions and drawbacks. The main steps of an image registration procedure are also discussed. Different performance measures are presented that determine the registration quality and accuracy. The scope for the future research are presented as well.

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Section: B) Fuzzy Setsmentioning

confidence: 99%

Image Registration Techniques: A Survey

Nag¹

2017

Preprint

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“…Thus MR-PET image fusion can accurately interpret the location and range of disease with combined information. However, in order to fuse MR-PET brain images, image registration [1][2][3][4][5][6][7] determining the relationship of correspondence between two images with different resolution, position, and orientation is necessary.…”

Section: Introductionmentioning

confidence: 99%

“…Since the computation of center of gravity is usually not accurate in blurry ones like PET images, this method is used primarily as a coarse pre-registration. Surface-based registration [3][4] requires delineation of corresponding surfaces in each of the images separately. Y.Hata et al and L.Y.Hsu et al proposed the methods to align of the brain surface extracted by automatic segmentation algorithms in MR and PET images.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Surface- and Voxel-Based Registration for MR-PET Brain Fusion

Lee

Hong

2005

Image Analysis and Processing – ICIAP 2005

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In this paper, we propose a novel technique of registration using hybrid approach for MR-PET brain image fusion. Hybrid approach uses merits of surface-and voxel-based registration. Thus, our method measures similarities using voxel intensities in MR images corresponding to the feature points of the brain in PET images. Proposed method selects the brain threshold using histogram accumulation ratio in PET images. And then, we automatically segment the brain using the inverse region growing with pre-calculated threshold and extract the feature points of the brain using sharpening filter in PET images. In order to find the optimal location for registration, we evaluate the Hybrid-based Cross-Correlation using the voxel intensities in MR images corresponding to the feature points in PET images. In our experiments, we evaluate our method using software phantom and clinical datasets in the aspect of visual inspection, accuracy, robustness, and computation time. Experimental results show that our method is dramatically faster than the voxel-based registration and more accurate than the surface-based registration. In particular, our method can robustly align two datasets with large geometrical displacement and noise at optimal location.

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Medical image registration in computational intelligence framework: a review

Ramirez

Durdle

Raso³

CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH374

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Registration of multi-modality medical images by soft computing approach

Cited by 4 publications

References 16 publications

Image Registration Techniques: A Survey

Image Registration Techniques: A Survey

Hybrid Surface- and Voxel-Based Registration for MR-PET Brain Fusion

Medical image registration in computational intelligence framework: a review

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