Fuzzy-based Vascular Structure Enhancement in Time-of-Flight MRA Images for Improved Segmentation

Abstract

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. 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. 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. The proposed method can automatically fuse the benefits of intensity and vesselness information and can improve the results of following cerebrovascular segmentations.