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Comparative Analysis of Adaptive Vessel Segmentation—Cerebral Arteriovenous Malformation

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DOI: 10.4236/jbise.2015.812076    4,104 Downloads   4,459 Views   Citations


Aim: Neurovascular abnormalities are extremely complex, due to the multitude of factors acting simultaneously on cerebral hemodynamics. Cerebral Arteriovenous Malformation (CAVM) hemo-dynamic in one of the vascular abnormality condition results changes in the vessels structures and hemodynamics in blood vessels. The challenge is segmenting accurate vessel region to measure hemodynamics of CAVM patients. The clinical procedure is in-vivo method to measure hemodynamics. The catheter-based procedure is difficult, as it is sometimes difficult to reach vessels sub-structures. Methods: In this paper, we have proposed adaptive vessel segmentation based on threshold technique for CAVM patients. We have compared different adaptive methods for vessel segmentation of CAVM structures. The sub-structures are modeled using lumped model to measure hemodynamics non-invasively. Results: Twenty-three CAVM patients with 150 different vessel locations of DSA datasets were studied as part of the adaptive segmentation. 30 simulated data has been evaluated for more than 150 vessels locations for sub-segmentation of vessels. The segmentation results are evaluated with accuracy of 93%. The computed p-value is smaller than the significance level 0.05. Conclusion: The adaptive segmentation using threshold based produces accurate vessel segmentation, results in better accuracy of hemodynamic measurements for DSA images for CAVM patients. The proposed adaptive segmentation helps clinicians to measure hemodynamic non-invasively for the segmented sub-structures of vessels.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Kumar, Y. , Mehta, S. and Ramachandra, M. (2015) Comparative Analysis of Adaptive Vessel Segmentation—Cerebral Arteriovenous Malformation. Journal of Biomedical Science and Engineering, 8, 797-804. doi: 10.4236/jbise.2015.812076.


[1] Babin, D., et al. (2013) Brain Blood Vessel Segmentation Using Line-Shaped Profiles. Physics in Medicine and Biology, 58, 8041-8061.
[2] Liu, I., et al. (1993) Recursive Tracking of Vascular Networks in Angiograms Based on the Detection-Deletion Scheme. IEEE Transactions on Medical Imaging, 12, 334-341.
[3] Sang, N., et al. (2007) Knowledge Based Adaptive Thresholding Segmentaion of Digital Subtraction Angiography Images. Image and Vision Computing, 25, 1263-1270.
[4] Kumar, Y.K., Mehta, S.B. and Ramachandra, M. (2014) Vascular Segmentation of Cerebral AVM. AIR, 2, 52-57.
[5] Yang, X.L., et al. (2012) An Improved Median-Based Otsu Image Thresholding Algorithm. AASRI Procedia, 3, 468-473.
[6] Kumar, Y.K., Mehta, S. and Ramachandra, M. (2013) Review Paper: Cerebral Arteriovenous Malformations Modelling. International Journal of Scientific and Engineering Research, 4, 129-139.
[7] Tsai, C.-M., et al. (2015) Identifying Regions of Interest in Reading an Image. Displays, 39, 33-41.
[8] Guglielmi, G. (2006) Electrical Models in the Analysis of Hemodynamic Characteristics of Arteriovenous Malformations. Interventional Neuroradiology, 12, 9-15.
[9] Kumar, Y.K., Mehta, S. and Ramachandra, M. (2014) Multimodality Vessel Modelling Analysis for Cerebral Arteriovenous Malformation. Journal of Behavioral and Brain Science, 2, 23-26.
[10] Yu, S., et al. (2012) Noncontrast Dynamic MRA in Intracranial Arteriovenous Malformation (AVM): Comparison with Time of Flight (TOF) and Digital Subtraction Angiography (DSA). Magnetic Resonance Imaging, 30, 869-877.
[11] Betanzosa, A., Varelaa, A. and Martinez, C. (2000) Analysis and Evolution of Hard and Fuzzy Clustering Segmentation Techniques in Burned Patient Images. Image and Vision Computing, 18, 1045.
[12] Tsai, Y.-C., et al. (2015) Automatic Segmentation of Vessels from Angiogram Sequences Using Adaptive Feature Transformation. Computers in Biology and Medicine, 62, 239-253.
[13] Fic, A.M., Ingham, D.B., Ginalski, M.K., Nowak, A.J. and Wrobel, L.C. (2014) Modelling and Optimisation of the Operation of a Radiant Warmer. Medical Engineering & Physics, 36, 81-87.
[14] Lorenz, C., Carlsen, I., Buzug, T., Fassnacht, C. and Weese, J. (1997) Multi-Scale Line Segmentation with Automatic Estimation of Width, Contrast and Tangential Direction in 2D and 3D Medical Images. Lecture Notes in Computer Science, 1205, 233-242.
[15] Socher, R., Barbu, A. and Comaniciu, D. (2008) A Learning Based Hierarchical Model for Vessel Segmentation. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, 14-17 May 2008, 1055-1058.
[16] Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M. and Goldbaum, M. (1989) Detection of Blood Vessels in Retinal Images Using Two-Dimensional Matched Filters. IEEE Transactions on Medical Imaging, 8, 263-269.
[17] Martínez-Pérez, M., Hughes, A., Stanton, A., Thom, S., Bharath, A. and Parker, K. (1999) Scale-Space Analysis for the Characterisation of Retinal Blood Vessels. In: Taylor, C. and Colchester, A., Eds., Medical Image Computing and Computer-Assisted Intervention—MICCAI’99, 90-97.

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