Integrated visualization of brain anatomy and cerebral blood vessels

Vandermeulen, Dirk; Plets, P; Ramkers, Steven; Suetens, Paul; Marchal, Guy

doi:10.1145/147130.147146

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Vandermeulen et al 1 presented an integrated display of cerebral blood vessels and brain structure using 3D CT, MR and MRA images. Nonlinear morphological filters were applied to the MRA images to selectively enhance the blood vessel signal while suppressing the surrounding tissue.…”

Section: Introductionmentioning

confidence: 99%

Integrated registration and visualization of MR and PET brain images

2004

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Different imaging modalities give insight to vascular, anatomical and functional information that assists diagnosis and treatment planning in medicine. Depending on the clinical requirement, it is often not sufficient to consider anatomical and functional information separately but to superimpose images of different modalities. However it would often provide unreliable results since functional modalities have low sampling resolution. In this paper, we present a novel technique of improving an image fusion quality and speed by integrating voxel-based registration and consecutive visualization. In the first part, we discuss a voxel-based registration using mutual information including gradient measure to consider spatial information in the images and thereby provide a much more general and reliable measure. In the second part, we propose a volume rendering technique for generating high-quality images rapidly without specialized hardware. Fusion of MR and PET brain images are presented for visual validation for the proposed methods. Our method offers a robust technique to fuse anatomical and functional modalities which allow direct functional to structural correlation analysis.

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Section: Introductionmentioning

confidence: 99%

Integrated registration and visualization of MR and PET brain images

2004

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“…In its simplest form this means that the resulting image is composed of the combination of the separate 2D projection of both datasets [10], using e.g. blending, but also the combination of volume rendering techniques with Maximum Intensity Projection [11] can also be considered to be a member of this class. The benefit of this method is the fact that no (relevant) information can be hidden because of occlusion.…”

Section: Introductionmentioning

confidence: 99%

Silhouette Fusion of Vascular and Anatomical Volume Data

Ruijters

Babić

Romeny

et al.

3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006.

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