KEYWORDS: Detection and tracking algorithms, Medical imaging, Brain, Spine, Magnetic resonance imaging, 3D applications, Neuroimaging, Data analysis, 3D image processing, Imaging systems
One of primary challenges in the medical image data analysis is the ability to handle abnormal, irregular and/or
partial cases. In this paper, we present two different robust algorithms towards the goal of automatic planar
primitive detection in 3D volumes. The overall algorithm is a bottoms-up approach starting with the anatomic
point primitives (or landmarks) detection. The robustness in computing the planar primitives is built in through
both a novel consensus-based voting approach, and a random sampling-based weighted least squares regression
method. Both these approaches remove inconsistent landmarks and outliers detected in the landmark detection
step. Unlike earlier approaches focused towards a particular plane, the presented approach is generic and can be
easily adapted to computing more complex primitives such as ROIs or surfaces. To demonstrate the robustness
and accuracy of our approach, we present extensive results for automatic plane detection (Mig-Sagittal and
Optical Triangle planes) in brain MR-images. In comparison to ground truth, our approach has marginal errors
on about 90 patients. The algorithm also works really well under adverse conditions of arbitrary rotation and
cropping of the 3D volume. In order to exhibit generalization of the approach, we also present preliminary results
on intervertebrae-plane detection for 3D spine MR application.
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