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Comparison of 3-Segmentation Techniques for Intraventricular and Intracerebral Hemorrhages in Unenhanced Computed Tomography Scans

KN, Bhanu Prakash PhD*; Hu, Jianbo ME*; Morgan, Timothy C. BSc; Hanley, Daniel MD; Nowinski, Wieslaw L. PhD, DSc*

Journal of Computer Assisted Tomography: January/February 2012 - Volume 36 - Issue 1 - p 109–120
doi: 10.1097/RCT.0b013e318245c1fa

Objectives The Clot Lysis Evaluating Accelerated Resolution of Intraventricular Hemorrhage Trial phase III is a multicenter, randomized clinical trial in the management and treatment of subjects with small intracerebral hemorrhage and large intraventricular hemorrhage. Accurate localization, segmentation, and quantification of hemorrhage are necessary for decision making and treatment. Our studies are aimed at developing algorithms for accurate and automatic hemorrhage segmentation for this trial.

Methods Two hundred one computed tomography scans of 41 patients with 2.5- to 10-mm slice thickness from 10 hospitals were used. Techniques based on thresholding, clustering, and graph theory modified using textural energy–based normalization were used along with preprocessing (filtering, skull stripping) and postprocessing (artifact removal). The segmented results of each method are compared with the ground truths.

Results The median sensitivity, specificity, and dice statistical index (DSI) are 86.19%, 99.94%, and 0.8655 for modified thresholding; 83.23%, 99.93%, and 0.8410 for modified fuzzy C-means; and 87.28%, 99.81%, and 0.7917 for modified normalized cut method, respectively. The preprocessing and postprocessing enhanced the DSI by 10% and 3%, respectively. Usage of textural energy along with the Hounsfield value in the modified methods increased the DSI by about 8% to 10%. The methods reduced the time needed for processing from 20 to 30 minutes to 2 to 3 minutes per case.

Conclusions The modified thresholding provided the highest accuracy, least computation time, and implementation complexity compared with other 2 methods. The method reduces the time to localize and segment the hemorrhagic regions and also provides quantitative information that is critical to precise therapeutic decision making.

From the *Biomedical Imaging Lab, Singapore Bio-imaging Consortium, Agency for Science, Technology and Research, Singapore; and †Division of Brain Injury Outcomes, Department of Neurology, and ‡Department of Neurology, Johns Hopkins University, Baltimore, MD.

Received for publication September 29, 2011; accepted December 8, 2011.

Reprints: Bhanu Prakash, KN, PhD, Biomedical Imaging Lab, Singapore Bio-imaging Consortium, Agency for Science, Technology and Research, #07-01, MATRIX, 30, Biopolis St, Singapore 138671 (e-mail:

The authors report no conflicts of interest.

© 2012 Lippincott Williams & Wilkins, Inc.