Rationale and objectives: A computerized scheme for automated detection of unruptured intracranial aneurysms in magnetic resonance angiography was developed based on the use of a three-dimensional selective enhancement filter for dots (aneurysms).
Materials and methods: Twenty-nine cases with 36 unruptured aneurysms (diameter, 3 to 26 mm; mean, 6.6 mm) and 31 non-aneurysm cases were used in this study. The isotropic 3-dimensional magnetic resonance angiography images with 400 x 400 x 128 voxels (voxel size, 0.5 mm) were processed by use of the selective enhancement filter. The initial candidates were identified by use of a multiple gray-level thresholding technique on the dot-enhanced images and a region-growing technique with monitoring some image features. All candidates were classified into four types of candidates according to the size and local structures based on the effective diameter and skeleton image of each candidate (ie, large candidates and three types of small candidates including short-branch type, single-vessel type, and bifurcation type). In each group, a number of false-positives were removed by use of different rules on localized image features related to gray levels and morphology. Linear discriminant analysis was used for further removal of false-positives.
Results: With this computer-aided diagnostic scheme, all of 36 aneurysms were correctly detected with 2.4 false-positives per patient based on a leave-one-out-by-patient test method.
Conclusion: This computer-aided diagnostic system would be useful in assisting radiologists for the detection of intracranial aneurysms in magnetic resonance angiography.