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

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.

[1]  Eugene G Kholmovski,et al.  Correction of slab boundary artifact using histogram matching , 2002, Journal of magnetic resonance imaging : JMRI.

[2]  J. Alison Noble,et al.  An adaptive segmentation algorithm for time-of-flight MRA data , 1999, IEEE Transactions on Medical Imaging.

[3]  J. Mohr,et al.  Brain arteriovenous malformations in adults , 2005, The Lancet Neurology.

[4]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[5]  Aly A. Farag,et al.  Cerebrovascular segmentation from TOF using stochastic models , 2006, Medical Image Anal..

[6]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[7]  H. Handels,et al.  Territorial and Microvascular Perfusion Impairment in Brain Arteriovenous Malformations , 2008, American Journal of Neuroradiology.

[8]  William Schroeder,et al.  The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics , 1997 .

[9]  K Akazawa,et al.  Accuracy in the Diagnostic Prediction of Acute Appendicitis Based on the Bayesian Network Model , 2007, Methods of Information in Medicine.

[10]  H Handels,et al.  Integrated segmentation and non-linear registration for organ segmentation and motion field estimation in 4D CT data. , 2009, Methods of information in medicine.

[11]  Jürgen Weese,et al.  A Multi-scale Line Filter with Automatic Scale Selection Based on the Hessian Matrix for Medical Image Segmentation , 1997, Scale-Space.

[12]  P Degoulet,et al.  Quantifying stenosis in renal arteriograms: a fuzzy syntactic analysis. , 1999, Methods of information in medicine.

[13]  J. McNeil,et al.  Incidence of the Major Stroke Subtypes: Initial Findings From the North East Melbourne Stroke Incidence Study (NEMESIS) , 2001, Stroke.

[14]  Ross T. Whitaker,et al.  Variable-conductance, level-set curvature for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[15]  Pierre Hellier,et al.  Combining fuzzy logic and level set methods for 3D MRI brain segmentation , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[16]  H Handels,et al.  Medical Image Computing for Computer-supported Diagnostics and Therapy , 2009, Methods of Information in Medicine.

[17]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[18]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[19]  Guido Gerig,et al.  Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1998, Medical Image Anal..