Ideal Midline Detection Using Automated Processing of Brain CT Image

Xuguang Qi, Ashwin Belle, Sharad Shandilya, Wenan Chen, Charles Cockrell, Yang Tang, Kevin R. Ward, Rosalyn H. Hargraves, Kayvan Najarian

Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, USA.
Department of Computer Science, School of Engineering, Virginia Commonwealth University, Richmond, USA.
Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, USA.
Department of Emergency Medicine and Michigan Critical Injury and Illness Research Center, University of Michigan, Ann Arbor, USA.
Department of Radiology, School of Medicine, Virginia Commonwealth University, Richmond, USA.

DOI: 10.4236/ojmi.2013.32007                

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.

Keywords

Ideal Midline; Slice Selection; Exhaustive Symmetric Search; Global Rotation

Conflicts of Interest

The authors declare no conflicts of interest.

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