Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words
This work addresses the need for automated detection of mTBI from medical images, which could aid in diagnosis and monitoring, but it is incremental as it builds on existing computer vision methods.
The paper tackled the problem of detecting mild traumatic brain injury (mTBI) from MR images by using a bag-of-visual-words technique to extract features from brain patches, achieving better accuracy than previous methods that used simple mean value features.
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.