[1]
|
Babin, D., et al. (2013) Brain Blood Vessel Segmentation Using Line-Shaped Profiles. Physics in Medicine and Biology, 58, 8041-8061. http://dx.doi.org/10.1088/0031-9155/58/22/8041
|
[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. http://dx.doi.org/10.1109/42.232264
|
[3]
|
Sang, N., et al. (2007) Knowledge Based Adaptive Thresholding Segmentaion of Digital Subtraction Angiography Images. Image and Vision Computing, 25, 1263-1270. http://dx.doi.org/10.1016/j.imavis.2006.07.026
|
[4]
|
Kumar, Y.K., Mehta, S.B. and Ramachandra, M. (2014) Vascular Segmentation of Cerebral AVM. AIR, 2, 52-57. http://dx.doi.org/10.9734/AIR/2014/7044
|
[5]
|
Yang, X.L., et al. (2012) An Improved Median-Based Otsu Image Thresholding Algorithm. AASRI Procedia, 3, 468-473. http://dx.doi.org/10.1016/j.aasri.2012.11.074
|
[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. http://dx.doi.org/10.1016/j.displa.2015.08.001
|
[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. http://dx.doi.org/10.4236/jbbs.2014.41003
|
[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. http://dx.doi.org/10.1016/j.mri.2012.02.027
|
[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. http://dx.doi.org/10.1016/S0262-8856(00)00045-7
|
[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. http://dx.doi.org/10.1016/j.compbiomed.2015.04.029
|
[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. http://dx.doi.org/10.1016/j.medengphy.2013.10.003
|
[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. http://dx.doi.org/10.1007/BFb0029242
|
[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. http://dx.doi.org/10.1109/isbi.2008.4541181
|
[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. http://dx.doi.org/10.1109/42.34715
|
[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.
|