Purpose: Various kinds of enhancement filters have been developed in computer-aided diagnostic (CAD) frameworks for asymptomatic intracranial aneurysms in magnetic resonance angiography (MRA). However, many bending or branching portions on vessels are also enhanced by the conventional filters as false positives in 3.0 T MRA, which can visualize smaller vessels compared with 1.5 T MRA. To overcome this problem, this study focused on developing an ellipsoid convex enhancement (ECE) filter, which can selectively enhance aneurysms while reducing false positive contrasts on bending or branching portions on vessels, for detection of asymptomatic intracranial aneurysm candidates in CAD frameworks. Methods: The ECE filter was mathematically designed to enhance various convex regions in the intensity space such as convex aneurysms, in which the ratio of the shortest and longest diameters for aneurysms corresponds to the ratio of reciprocals of the square roots of the first and third eigenvalues of a Hessian matrix. The proposed ECE filter was evaluated by measuring an average contrast for false positive models and free-response receiver operating characteristic curves between two simple CAD frameworks using the ECE and conventional filters based on a leave-one-out-by-patient test. MRA images for thirty patients (male: 10, female: 20; age: 4886 yr, mean: 69.2) with 31 unruptured aneurysms (longest diameter: 2.05.5 mm, mean: 3.7 mm) were selected for this study. Results: The average contrast for false positive models was reduced by 51.4% using the ECE filter, compared with the conventional filter for the convex regions with ratios of the shortest and longest diameters less than 0.4. The number of false positives per case was decreased from 41.1 to 22.8 on average at a sensitivity of 87% by using the ECE filter. Conclusions: The ECE filter would be useful for boosting the performance of the CAD framework of asymptomatic intracranial aneurysms by providing higher contrast aneurysms and lower contrast false positives such as bending or branching portions on vessels.
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging