Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Incorporating Connected Region Labelling into Automatic Image Registration Using Mutual Information
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Faster and Better: A Machine Learning Approach to Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
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Radiological imaging of prostate is becoming more popular among researchers and clinicians in searching for diseases, primarily cancer. Scans might be acquired at different times, with patient movement between scans, or with different equipment, resulting in multiple datasets that need to be registered. For this issue, we introduce a registration method using anatomical feature-guided mutual information. Prostate scans of the same patient taken in three different orientations are first aligned for the accurate detection of anatomical features in 3D. Then, our pipeline allows for multiple modalities registration through the use of anatomical features, such as the interior urethra of prostate and gland utricle, in a bijective way. The novelty of this approach is the application of anatomical features as the pre-specified corresponding landmarks for prostate registration. We evaluate the registration results through both artificial and clinical datasets. Registration accuracy is evaluated by performing statistical analysis of local intensity differences or spatial differences of anatomical landmarks between various MR datasets. Evaluation results demonstrate that our method statistics-significantly improves the quality of registration (compared to the non-feature guided registration). Although this strategy is tested for MRI-guided brachytherapy, the preliminary results from our experiments suggest that it can be also applied to other settings such as transrectal ultrasound-guided or CT-guided therapy, where the integration of preoperative MRI may have a significant impact upon treatment planning and guidance.