Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Methods for Ridge and Valley Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integration of valley orientation distribution for polyp region identification in colonoscopy
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
Towards automatic polyp detection with a polyp appearance model
Pattern Recognition
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This work aims at the segmentation of colonoscopy images into a minimum number of informative regions. Our method performs in a way such, if a polyp is present in the image, it will be exclusively and totally contained in a single region. This result can be used in later stages to classify regions as polyp-containing candidates. The output of the algorithm also defines which regions can be considered as noninformative. The algorithm starts with a high number of initial regions and merges them taking into account the model of polyp appearance obtained from available data. The results show that our segmentations of polyp regions are more accurate than state-of-the-art methods.