DocumentCode :
1112080
Title :
Morphological Characterization of Intracranial Aneurysms Using 3-D Moment Invariants
Author :
Millán, R.D. ; Dempere-Marco, L. ; Pozo, J.M. ; Cebral, J.R. ; Frangi, A.F.
Author_Institution :
Pompeu Fabra Univ., Barcelona
Volume :
26
Issue :
9
fYear :
2007
Firstpage :
1270
Lastpage :
1282
Abstract :
Rupture of intracranial saccular aneurysms is the most common cause of spontaneous subarachnoid hemorrhage, which has significant morbidity and mortality. Although there is still controversy regarding the decision on which unruptured aneurysms should be treated, this is based primarily on their size. Nonetheless, many large lesions do not rupture whereas some small ones do. It is commonly accepted that hemodynamical factors are important to better understand the natural history of cerebral aneurysms. However, it might not always be practical to carry out a detailed computational analysis of such factors if a prompt assessment is required. Since shape is likely to be dependent on the balance between hemodynamic forces and the aneurysmal surrounding environment, an appropriate morphological 3-D characterization is likely to provide a practical surrogate to quickly evaluate the risk of rupture. In this paper, an efficient and novel methodology for 3-D shape characterization of cerebral aneurysms is described. The aneurysms are isolated by taking into account a portion of their adjacent vessels. Two methods to characterize the morphology of the aneurysms models using moment invariants have been considered: geometrical moment invariants (GMI) and Zernike moment invariants (ZMI). The results have been validated in a database containing 53 patients with a total of 31 ruptured aneurysms and 24 unruptured aneurysms. It has been found that ZMI indices are more robust than GMI, and seem to provide a reliable way to discriminate between ruptured and unruptured aneurysms. Correct rupture prediction rates of sime80% were achieved in contrast to 66% that is found when the aspect ratio index is considered.
Keywords :
blood vessels; computerised tomography; diagnostic radiography; integral equations; medical image processing; method of moments; 3D moment invariants; 3D shape characterization; GMI indices; ZMI indices; Zernike moment invariants; aneurysm rupture risk; aneurysmal surrounding environment; cerebral aneurysms; computed tomography angiography; geometrical moment invariants; hemodynamic forces; hemodynamical factors; intracranial aneurysms; intracranial saccular aneurysm rupture; morphological 3D characterization; spontaneous subarachnoid hemorrhage; Aneurysm; Hemodynamics; Hemorrhaging; History; Lesions; Morphology; Robustness; Shape; Solid modeling; Spatial databases; 3-D Zernike moments; Intracranial aneurysms; morphological characterization; Algorithms; Aneurysm, Dissecting; Artificial Intelligence; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Intracranial Aneurysm; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2007.901008
Filename :
4298153
Link To Document :
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