OBJECTIVES: 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. METHODS: After preprocessing of a TOF dataset, the corresponding vesselness image is computed. The role of the fuzzy logic is to voxel-wisely fuse the intensity information from the TOF dataset with the corresponding vesselness information based on an analytically designed rule base. The resulting fuzzy parameter image can then be used for improved cerebrovascular segmentation. RESULTS: Six datasets, manually segmented by medical experts, were used for evaluation. Based on TOF, vesselness and fused fuzzy parameter images, the vessels of each patient were segmented using optimal thresholds computed by maximizing the agreement to manual segmentations using the Tanimoto coefficient. The results showed an overall improvement of 0.054 (fuzzy vs. TOF) and 0.079 (fuzzy vs. vesselness). Furthermore, the evaluation has shown that the method proposed yields better results than statistical Bayes classification. CONCLUSION: The proposed method can automatically fuse the benefits of intensity and vesselness information and can improve the results of following cerebrovascular segmentations.
OBJECTIVES: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. METHODS: After preprocessing of a TOF dataset, the corresponding vesselness image is computed. The role of the fuzzy logic is to voxel-wisely fuse the intensity information from the TOF dataset with the corresponding vesselness information based on an analytically designed rule base. The resulting fuzzy parameter image can then be used for improved cerebrovascular segmentation. RESULTS: Six datasets, manually segmented by medical experts, were used for evaluation. Based on TOF, vesselness and fused fuzzy parameter images, the vessels of each patient were segmented using optimal thresholds computed by maximizing the agreement to manual segmentations using the Tanimoto coefficient. The results showed an overall improvement of 0.054 (fuzzy vs. TOF) and 0.079 (fuzzy vs. vesselness). Furthermore, the evaluation has shown that the method proposed yields better results than statistical Bayes classification. CONCLUSION: The proposed method can automatically fuse the benefits of intensity and vesselness information and can improve the results of following cerebrovascular segmentations.
Authors: M H Schönfeld; V Schlotfeldt; N D Forkert; E Goebell; M Groth; E Vettorazzi; Y D Cho; M H Han; H-S Kang; J Fiehler Journal: Clin Neuroradiol Date: 2014-08-27 Impact factor: 3.649
Authors: N D Forkert; J Fiehler; M Schönfeld; J Sedlacik; J Regelsberger; H Handels; T Illies Journal: Clin Neuroradiol Date: 2012-08-26 Impact factor: 3.649
Authors: Wilby Williamson; Adam J Lewandowski; Nils D Forkert; Ludovica Griffanti; Thomas W Okell; Jill Betts; Henry Boardman; Timo Siepmann; David McKean; Odaro Huckstep; Jane M Francis; Stefan Neubauer; Renzo Phellan; Mark Jenkinson; Aiden Doherty; Helen Dawes; Eleni Frangou; Christina Malamateniou; Charlie Foster; Paul Leeson Journal: JAMA Date: 2018-08-21 Impact factor: 56.272
Authors: Nils Daniel Forkert; Till Illies; Einar Goebell; Jens Fiehler; Dennis Säring; Heinz Handels Journal: Int J Comput Assist Radiol Surg Date: 2013-03-07 Impact factor: 2.924
Authors: Ulrike Löbel; Nils Daniel Forkert; Peter Schmitt; Thorsten Dohrmann; Maria Schroeder; Tim Magnus; Stefan Kluge; Christina Weiler-Normann; Xiaoming Bi; Jens Fiehler; Jan Sedlacik Journal: PLoS One Date: 2016-11-01 Impact factor: 3.240