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Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury
- Source :
- IEEE transactions on medical imaging, vol 33, iss 1
- Publication Year :
- 2014
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2014.
-
Abstract
- Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care.
- Subjects :
- Computer science
computer.software_genre
Pattern Recognition, Automated
Computer-Assisted
Engineering
Injury - Trauma - (Head and Spine)
Image texture
Recurrence
Models
magnetic resonance imaging
Brain injury
screening and diagnosis
Radiological and Ultrasound Technology
medicine.diagnostic_test
Statistical
Magnetic Resonance Imaging
Computer Science Applications
Detection
Nuclear Medicine & Medical Imaging
Injury - Trauma
Neurological
Biomedical Imaging
medicine.symptom
Algorithms
4.2 Evaluation of markers and technologies
Automated
medicine.medical_specialty
Physical Injury - Accidents and Adverse Effects
Traumatic brain injury
Models, Neurological
Feature extraction
Bioengineering
Context (language use)
Traumatic Brain Injury (TBI)
Pattern Recognition
Machine learning
Sensitivity and Specificity
Basic Behavioral and Social Science
Lesion
Physical medicine and rehabilitation
Clinical Research
Information and Computing Sciences
Image Interpretation, Computer-Assisted
Behavioral and Social Science
medicine
Animals
Humans
Computer Simulation
Electrical and Electronic Engineering
Image Interpretation
Eye Disease and Disorders of Vision
Traumatic Head and Spine Injury
Models, Statistical
contextual modeling
business.industry
Neurosciences
Reproducibility of Results
Magnetic resonance imaging
Image Enhancement
medicine.disease
textural modeling
Rats
Brain Disorders
4.1 Discovery and preclinical testing of markers and technologies
Support vector machine
Brain Injuries
Subtraction Technique
Injury (total) Accidents/Adverse Effects
Artificial intelligence
Injury - Traumatic brain injury
business
computer
Software
Subjects
Details
- ISSN :
- 1558254X and 02780062
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Medical Imaging
- Accession number :
- edsair.doi.dedup.....221b403491e8809319e11057f7681fa3