Comparison of GWO-SVM and Random Forest Classifiers in a LevelSet based approach for Bladder wall segmentation and characterisation using MR images.
Rania TriguiMouloud AdelMathieu Di BisceglieJulien WojakJessica PinolAlice FaureKathia ChaumoitrePublished in: IPTA (2022)
Keyphrases
- mr images
- random forest
- fold cross validation
- accurate segmentation
- medical images
- ensemble classifier
- brain mr images
- decision trees
- partial volume
- magnetic resonance
- feature set
- support vector
- brain tumors
- intensity distribution
- random forests
- intensity inhomogeneity
- feature ranking
- magnetic resonance images
- mr imaging
- manual segmentation
- svm classifier
- bias field
- cardiac magnetic resonance
- contrast enhanced
- feature selection
- mri data
- ensemble methods
- training data
- support vector machine svm
- prostate cancer
- support vector machine
- image data
- image segmentation
- segmentation method
- multi label
- segmentation algorithm
- anatomical structures
- feature vectors
- training set
- base classifiers
- level set
- cancer classification
- knn
- deformable models
- naive bayes
- class labels
- ct images
- clinical applications
- text categorization
- kernel function
- magnetic resonance imaging
- logistic regression
- feature extraction
- computer vision