Fuzzy-based vascular structure enhancement in Time-of-Flight MRA images for improved segmentation.
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Fuzzy-based vascular structure enhancement in Time-of-Flight MRA images for improved segmentation. / Forkert, Nils; Schmidt-Richberg, Alexander; Fiehler, Jens; Illies, Till; Möller, D; Handels, Heinz; Säring, Dennis.
In: METHOD INFORM MED, Vol. 50, No. 1, 1, 2011, p. 74-83.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Fuzzy-based vascular structure enhancement in Time-of-Flight MRA images for improved segmentation.
AU - Forkert, Nils
AU - Schmidt-Richberg, Alexander
AU - Fiehler, Jens
AU - Illies, Till
AU - Möller, D
AU - Handels, Heinz
AU - Säring, Dennis
PY - 2011
Y1 - 2011
N2 - Cerebral vascular malformations might lead to strokes due to occurrence of ruptures. The rupture risk is highly related to the individual vascular anatomy. The 3D Time-of-Flight (TOF) MRA technique is a commonly used non-invasive imaging technique for exploration of the vascular anatomy. Several clinical applications require exact cerebrovascular segmentations from this image sequence. For this purpose, intensity-based segmentation approaches are widely used. Since small low-contrast vessels are often not detected, vesselness filter-based segmentation schemes have been proposed, which contrariwise have problems detecting malformed vessels. In this paper, a fuzzy logic-based method for fusion of intensity and vesselness information is presented, allowing an improved segmentation of malformed and small vessels at preservation of advantages of both approaches.
AB - Cerebral vascular malformations might lead to strokes due to occurrence of ruptures. The rupture risk is highly related to the individual vascular anatomy. The 3D Time-of-Flight (TOF) MRA technique is a commonly used non-invasive imaging technique for exploration of the vascular anatomy. Several clinical applications require exact cerebrovascular segmentations from this image sequence. For this purpose, intensity-based segmentation approaches are widely used. Since small low-contrast vessels are often not detected, vesselness filter-based segmentation schemes have been proposed, which contrariwise have problems detecting malformed vessels. In this paper, a fuzzy logic-based method for fusion of intensity and vesselness information is presented, allowing an improved segmentation of malformed and small vessels at preservation of advantages of both approaches.
M3 - SCORING: Journal article
VL - 50
SP - 74
EP - 83
JO - METHOD INFORM MED
JF - METHOD INFORM MED
SN - 0026-1270
IS - 1
M1 - 1
ER -