Methods Inf Med - Fuzzy-based vascular structure enhancement in Time-of-Flight MRA images for improved segmentation.

Tópicos

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Resumo

JECTIVES: 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.

Resumo Limpo

jectiv cerebr vascular malform might lead stroke due occurr ruptur ruptur risk high relat individu vascular anatomi d timeofflight tof mra techniqu common use noninvas imag techniqu explor vascular anatomi sever clinic applic requir exact cerebrovascular segment imag sequenc purpos intensitybas segment approach wide use sinc small lowcontrast vessel often detect vessel filterbas segment scheme propos contrariwis problem detect malform vessel paper fuzzi logicbas method fusion intens vessel inform present allow improv segment malform small vessel preserv advantag approachesmethod preprocess tof dataset correspond vessel imag comput role fuzzi logic voxelwis fuse intens inform tof dataset correspond vessel inform base analyt design rule base result fuzzi paramet imag can use improv cerebrovascular segmentationresult six dataset manual segment medic expert use evalu base tof vessel fuse fuzzi paramet imag vessel patient segment use optim threshold comput maxim agreement manual segment use tanimoto coeffici result show overal improv fuzzi vs tof fuzzi vs vessel furthermor evalu shown method propos yield better result statist bay classificationconclus propos method can automat fuse benefit intens vessel inform can improv result follow cerebrovascular segment

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