Feature-based brain mid-sagittal plane detection by RANSAC

Mid-sagittal plane passes through the border between the two hemispheres of a brain, which are roughly symmetric. Image-based detection of mid-sagittal plane has applications to a number of computer and human tasks, such as image registration and diagnosis. The problem requires robust methods to inherent asymmetries between the two hemispheres, pathalogical abnormalities that further degrade the hemispheric symmetry, and degradations in image quality. Furthermore, it is desirable to have a computationally feasible method because mid-sagittal plane detection is often a pre-processing step that is followed by more compute-intensive algorithms. In this paper, we introduce a novel feature-based mid-sagittal plane detection algorithm for MR brain images. The proposed method is robust even in the presence of very large abnormalities, can cope with outliers in the detected features, and is very fast. Its robustness to abnormalities stems from its hierarchical operation. A 3-D MR data is first processed as 1-D image lines, then as 2-D slices, and finally 3-D volume. This makes it possible to detect the mid-sagittal plane as long as two image lines are not affected by pathalogical abnormality, which is a significant improvement over the literature. Furthermore, the use of outlier-robust RANSAC algorithm for fitting a mid-sagittal line to the detected feature points in each slice provides robustness to the inaccuracies in the detected feature points.