Symbol-Based Machine Learning Approach for Supervised Segmentation of Follicular Lymphoma Images.
Milan ZormanPeter KokolMitja LenicJosé Luis Sánchez de la RosaJosé F. SigutSilvia AlayónPublished in: CBMS (2007)
Keyphrases
- machine learning
- segmentation algorithm
- segmentation method
- test images
- image analysis
- image data
- microscopic images
- edge detection
- input image
- accurate segmentation
- quantitative evaluation
- grey level
- supervised learning
- segmentation errors
- ground truth
- unsupervised segmentation
- image regions
- image features
- image classification
- three dimensional
- fully automatic
- feature selection
- segmented images
- piece wise
- segmentation accuracy
- microscopy images
- region growing
- image retrieval
- image segments
- bounding box
- adaptive thresholding
- image database
- symbol recognition
- image segmentation
- learning algorithm
- image segmentation algorithms
- positron emission tomography
- automatically segmented
- cell nuclei
- multiple objects
- intensity images
- pixel level
- watershed transform
- microscope images
- automatic segmentation
- unsupervised learning
- level set
- image slices
- watershed segmentation
- tubular structures
- medical images
- image registration
- multiscale
- histopathological images
- low depth of field