3-D segmentation of MR images of the head for 3-D display.
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3-D segmentation of MR images of the head for 3-D display. / Bomans, M; Hohne, K H; Tiede, U; Riemer, Martin.
in: IEEE T MED IMAGING, Jahrgang 9, Nr. 2, 2, 1990, S. 177-183.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - 3-D segmentation of MR images of the head for 3-D display.
AU - Bomans, M
AU - Hohne, K H
AU - Tiede, U
AU - Riemer, Martin
PY - 1990
Y1 - 1990
N2 - Algorithms for 3-D segmentation and reconstruction of anatomical surfaces from magnetic resonance imaging (MRI) data are presented. The 3-D extension of the Marr-Hildreth operator is described, and it is shown that its zero crossings are related to anatomical surfaces. For an improved surface definition, morphological filters-dilation and erosion-are applied. From these contours, 3-D reconstructions of skin, bone, brain, and the ventricular system can be generated. Results obtained with different segmentation parameters and surface rendering methods are presented. The fidelity of the generated images comes close to anatomical reality. It is noted that both the convolution and the morphological filtering are computationally expensive, and thus take a long time on a general-purpose computer. Another problem is assigning labels to the constituents of the head; in the current implementation, this is done interactively.
AB - Algorithms for 3-D segmentation and reconstruction of anatomical surfaces from magnetic resonance imaging (MRI) data are presented. The 3-D extension of the Marr-Hildreth operator is described, and it is shown that its zero crossings are related to anatomical surfaces. For an improved surface definition, morphological filters-dilation and erosion-are applied. From these contours, 3-D reconstructions of skin, bone, brain, and the ventricular system can be generated. Results obtained with different segmentation parameters and surface rendering methods are presented. The fidelity of the generated images comes close to anatomical reality. It is noted that both the convolution and the morphological filtering are computationally expensive, and thus take a long time on a general-purpose computer. Another problem is assigning labels to the constituents of the head; in the current implementation, this is done interactively.
M3 - SCORING: Zeitschriftenaufsatz
VL - 9
SP - 177
EP - 183
JO - IEEE T MED IMAGING
JF - IEEE T MED IMAGING
SN - 0278-0062
IS - 2
M1 - 2
ER -
