Automated unsupervised learning-based clustering approach for effective anomaly detection in brain magnetic resonance imaging (MRI).
Vishnuvarthanan GovindarajArunprasath ThiyagarajanM. Pallikonda RajasekaranYudong ZhangRajesh KrishnasamyPublished in: IET Image Process. (2020)
Keyphrases
- magnetic resonance imaging
- anomaly detection
- unsupervised learning
- mri data
- medical images
- intrusion detection
- magnetic resonance images
- partial volume effects
- medical imaging
- mri images
- supervised learning
- detecting anomalies
- diffusion tensor images
- brain segmentation
- brain tissue
- anomalous behavior
- intrusion detection system
- network intrusion detection
- self organizing maps
- breast mri
- dimensionality reduction
- network traffic
- expectation maximization
- one class support vector machines
- detect anomalies
- brain images
- functional mri
- feature selection
- neural network
- semi supervised
- negative selection algorithm
- medical image analysis
- text mining
- object recognition
- brain structures
- anatomical structures
- human brain
- image registration
- k means
- data analysis
- feature extraction