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has been cited by the following article:

• 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.