Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words

TL;DR

This study employs a machine learning approach using bag-of-visual-words features extracted from MRI scans to accurately identify patients with mild traumatic brain injury, improving detection over traditional mean value features.

Contribution

It introduces a novel application of bag-of-visual-words to MRI data for mTBI detection, demonstrating improved accuracy over previous methods.

Findings

BoW features outperform mean value features in classification accuracy

Feature selection enhances model performance

Method effectively distinguishes mTBI patients from healthy controls

Abstract

Mild traumatic brain injury (mTBI) is a growing public health problem with an estimated incidence of one million people annually in US. Neurocognitive tests are used to both assess the patient condition and to monitor the patient progress. This work aims to directly use MR images taken shortly after injury to detect whether a patient suffers from mTBI, by incorporating machine learning and computer vision techniques to learn features suitable discriminating between mTBI and normal patients. We focus on 3 regions in brain, and extract multiple patches from them, and use bag-of-visual-word technique to represent each subject as a histogram of representative patterns derived from patches from all training subjects. After extracting the features, we use greedy forward feature selection, to choose a subset of features which achieves highest accuracy. We show through experimental studies that BoW features perform better than the simple mean value features which were used previously.