Automatic detection and quantification of brain midline shift using anatomical marker model
Brain midline shift (MLS) is a significant factor in brain CT diagnosis. In this paper, we present a new method of automatically detecting and quantifying brain midline shift in traumatic injury brain CT images. The proposed method automatically picks out the CT slice on which midline shift can be observed most clearly and uses automatically detected anatomical markers to delineate the deformed midline and quantify the shift. For each anatomical marker, the detector generates five candidate points. Then the best candidate for each marker is selected based on the statistical distribution of features characterizing the spatial relationships among the markers. Experiments show that the proposed method outperforms previous methods, especially in the cases of large intra-cerebral hemorrhage and missing ventricles. A brain CT retrieval system is also developed based on the brain midline shift quantification results. © 2013 Elsevier Ltd.
Anatomatic marker model; Brain CT diagnosis; Brain midline shift; Midline shift detection and quantification
Artificial Intelligence and Robotics | Computer Sciences | Medicine and Health Sciences
Intelligent Systems and Decision Analytics
Computerized Medical Imaging and Graphics
LIU, Ruizhe; LI, Shimiao; SU, Bolan; TAN, Chew Lim; Tze-Yun LEONG; PANG, Boon Chuan; LIM, C.C. Tchoyoson; and LEE, Cheng Kiang.
Automatic detection and quantification of brain midline shift using anatomical marker model. (2014). Computerized Medical Imaging and Graphics. 38, (1), 1-14. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2922