TITLE:
Ideal Midline Detection Using Automated Processing of Brain CT Image
AUTHORS:
Xuguang Qi, Ashwin Belle, Sharad Shandilya, Wenan Chen, Charles Cockrell, Yang Tang, Kevin R. Ward, Rosalyn H. Hargraves, Kayvan Najarian
KEYWORDS:
Ideal Midline; Slice Selection; Exhaustive Symmetric Search; Global Rotation
JOURNAL NAME:
Open Journal of Medical Imaging,
Vol.3 No.2,
June
3,
2013
ABSTRACT: Brain ideal midline estimation is vital in medical image processing,
especially in analyzing the severity of a brain injury in clinical
environments. We propose an automated computer-aided ideal midline estimation
system with a two-step process. First, a CT Slice Selection Algorithm (SSA) can
automatically select an appropriate subset of slices from a large number of raw
CT images using the skull’s anatomical features. Next, an ideal midline
detection is implemented on the selected subset of slices. An exhaustive
symmetric position search is performed based on the anatomical features in the
detection. In order to enhance the accuracy of the detection, a global rotation
assumption is applied to determine the ideal midline by fully considering the
connection between slices. Experimental results of the multi-stage algorithm
were assessed on 3313 CT slices of 70 patients. The accuracy of the proposed
system is 96.9%, which makes it viable for use under clinical settings.