Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2014

Abstract

Clinical features found in brain CT scan images are widely used in traumatic brain injury (TBI) as indicators for Glasgow Outcome Scale (GOS) prediction. However, due to the lack of automated methods to measure and quantify the CT scan image features, the computerized prediction of GOS in TBI has not been well studied. This paper introduces an automated GOS prediction system for traumatic brain CT images. Different from most existing systems that perform the prognosis based on pre-processed data, our system directly works on brain CT scan images based on the image features. Our system can also be extended to large dataset with easy adaptation. For each new image of a CT scan series, our proposed system first makes use of sparse representation model that predicts the GOS of each CT image slice using Gabor features. Logistic regression, which integrates the GOS of each CT scan slice with a pre-trained model, is then applied to estimate the GOS score for the new case which contains multiple CT slices. Evaluation of the system has shown promising results in prediction of GOS of traumatic brain injury cases.

Keywords

Brain CT Scan, Glasgow Outcome Scale, Logistic Regression, Sparse Representation Classifier

Discipline

Computer Sciences | Health Information Technology | Numerical Analysis and Scientific Computing

Publication

ICPR 2014: 22nd International Conference on Pattern Recognition Proceedings: 24-28 August 2014, Stockholm, Sweden

First Page

3245

Last Page

3250

ISBN

9781479952083

Identifier

10.1109/ICPR.2014.559

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://dx.doi.org/10.1109/ICPR.2014.559

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