Use of gray value distribution of run lengths for texture analysis
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Rotation and scale invariant texture features using discrete wavelet packet transform
Pattern Recognition Letters
Texture information in run-length matrices
IEEE Transactions on Image Processing
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Elevated Intracranial Pressure (ICP) is a significant cause of mortality and long-term functional damage in traumatic brain injury (TBI). Current ICP monitoring methods are highly invasive, presenting additional risks to the patient. This paper describes a computerized non-invasive screening method based on texture analysis of computed tomography (CT) scans of the brain, which may assist physicians in deciding whether to begin invasive monitoring. Quantitative texture features extracted using statistical, histogram and wavelet transform methods are used to characterize brain tissue windows in individual slices, and aggregated across the scan. Support Vector Machine (SVM) is then used to predict high or normal levels of ICP using the most significant features from the aggregated set. Results are promising, providing over 80% predictive accuracy and good separation of the two ICP classes, confirming the suitability of the approach and the predictive power of texture features in screening patients for high ICP.