Abstract
In this paper, a fully automatic and computationally efficient midsagittal plane (MSP) extraction technique in brain magnetic resonance images (MRIs) has been proposed. Automatic detection of MSP in neuroimages can significantly aid in registration of medical images, asymmetric analysis, and alignment or tilt correction (recenter and reorientation) in brain MRIs. The parameters of MSP are estimated in two steps. In the first step, symmetric features and principal component analysis (PCA)-based technique is used to vertically align the bilateral symmetric axis of the brain. In the second step, PCA is used to achieve a set of parallel lines (principal axes) from the selected two-dimensional (2-D) elliptical slices of brain MRIs, followed by a plane fitting using orthogonal regression. The developed algorithm has been tested on 157 real T1-weighted brain MRI datasets including 14 cases from the patients with brain tumors. The presented algorithm is compared with a state-of-the-art approach based on bilateral symmetry maximization. Experimental results revealed that the proposed algorithm is fast (<1.04 s per MRI volume) and exhibits superior performance in terms of accuracy and precision (a mean z-distance of 0.336 voxels and a mean angle difference of 0.06).
Highlights
Segmentation of brain in magnetic resonance images (MRIs) is one of the difficult and crucial steps of clinical diagnostic tools in medical images
principal component analysis (PCA) is used to achieve a set of parallel lines from the selected two-dimensional (2-D) elliptical slices of brain MRIs, followed by a plane fitting using orthogonal regression
The developed algorithm has been tested on 157 real T1 -weighted brain MRI datasets including 14 cases from the patients with brain tumors
Summary
Segmentation of brain in magnetic resonance images (MRIs) is one of the difficult and crucial steps of clinical diagnostic tools in medical images. Ardekani et al [24] proposed an iterative local search-based algorithm that uses the cross-correlation between the voxels of either side of the estimated MSP This method failed on images having asymmetries due to pathological effects. Liu et al [18] computed the MSP by extracting the two-dimensional (2-D) symmetry axes on each slice using cross-correlation from an edge image, followed by plane fitting Another technique based on the similarity between two sides of the head volume using block matching was given by. After thresholding and elliptical area extraction, PCA is used to achieve a set of parallel lines (principal axes) from the selected 2-D slices of brain MRIs. In the second step, the roll angle and the plane offset (a perpendicular distance of MSP from the origin) have been computed by fitting a plane to these parallel lines using orthogonal regression [42].
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