Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering

Abstract

A semiautomatic technique to detect the aortic root in three-dimensional multi-slice computerised tomography images is proposed. Three steps are considered: conditioning, filtering, and detection. The conditioning is based on multi-planar reconstruction and it is required for reformatting the information to orthogonal planes to the aortic root. During the filtering, three nonlinear filters based on similarity enhancement, median and weighted median are considered to reduce noise and enhance the reformatted images. In the detection, the filtered volumes are processed with a clustering technique. Dice score, the point-to-mesh and the Hausdorff distances are used to compare the obtained results with respect to ground truth traced by a cardiologist. A clinical dataset of 90 volumes from 45 patients is used to validate the technique. The maximum Dice score (0.92), the minimum average point-to-mesh distance (0.96 mm) and the minimum average Hausdorff distance (4.80 mm) are obtained during preprocessed volumes segmentation using similarity enhancement.