H.E. Cline, C.L. Dumoulin, H.R. Hart, W.E. Lorensen, S. Ludke, 3D reconstruction of the brain from magnetic resonance images using a connectivity algorithm, Magnetic Resonance Imag. 5 (1987) 345-352.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....choice of the resolution of the detected contours. A low resolution segmentation is not accurate enough for our applications, while with a high resolution segmentation, too many non relevant details are detected. On another hand, procedures based on connectivity algorithms have also been proposed [6, 13, 14] but in those cases, different objects often remain connected by small links. Other algorithms require operator intervention during the segmentation process [ 15, 12, 31 ] Neural networks have also been used for MRI segmentation [24, 11, 35] but in many cases the computation time becomes ....

H.E. Cline, C.L. Dumoulin, H.R. Hart, W.E. Lorensen, S. Ludke, 3D reconstruction of the brain from magnetic resonance images using a connectivity algorithm, Magnetic Resonance Imag. 5 (1987) 345-352.

....were digitally transferred to the workstation (SPARC 20; Sun Microsystems, Inc. Mountain View, CA) via a computer network. Image processing: Each image was pre processed to reduce noise using anisotropic di#usion filtering [7] A segmentation based on signal intensities and voxel connectivity [8, 9] was then performed using one of the MR series (SPGR and MR angiogram) Imperfections in the 3D objects were corrected by manual editing. From these images, 3D models of the skin, brain, vessels, focal lesion and ventricles were reconstructed using the marching cubes algorithm and a surface ....

....performed using one of the MR series (SPGR and MR angiogram) Imperfections in the 3D objects were corrected by manual editing. From these images, 3D models of the skin, brain, vessels, focal lesion and ventricles were reconstructed using the marching cubes algorithm and a surface rendering method [8 10]. These objects were then integrated and displayed on a computer workstation (Ultra 1; Sun Microsystems, Inc. with a graphic accelerator (Creator 3D; Sun Microsystems Inc. and 3D display software (LAVA; Sun Microsystems, Inc. Each object thus may be rendered partially or completely transparent ....

Cline HE, Dumoulin CL, Lorensen WE, Hart HR, Ludke S: 3D reconstruction of the brain from magnetic resonance images using a connectivity algorithm. Magn Reson Imaging 5:345-352, 1989

....the original MC algorithm for the extraction of an isosurface. Moreover, the efficiency increases with higher resolution datasets as shown in Table 1. Another advantage of surface tracking is that a connected surface is always obtained. This eliminates the need to apply a connectivity algorithm [1, 2] following surface extraction in many applications. Any stray surface primitives arising due to noise in the data are also avoided. The final surface descriptions obtained from the two methods are identical if a single surface component is present in the data. Surface tracking allows selective ....

Cline, H. E., Dumoulin, C. L., Hart, H. R Jr., Lorensen, W. E., Ludke, S. 3D Reconstruction of the brain from magnetic resonance images using a connectivity algorithm. Magnetic Resonance Imaging 5, (1987), 345-352.

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H. E. Cline, C. L. Dumoulin, H. R. Hart Jr., W. E. Lorensen, and S. Ludke. 3D reconstruction of the brain from magnetic resonance images using a connectivity algorithm. Magnetic Resonance Imaging, 5:345--352, 1987.

No context found.

H. E. Cline, C. L. Dumoulin, W. E. Lorensen, H. R. Hart, Jr., and S. Ludke. 3D reconstruction of the brain from magnetic resonance images. Magnetic Resonance Imaging, July 1987.

No context found.

H. E. Cline, C. L. Dumoulin, H. R. Hart Jr., W. E. Lorensen, and S. Ludke. 3D reconstruction of the brain from magnetic resonance images using a connectivity algorithm. Magnetic Resonance Imaging, 5:345--352, 1987.

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