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For effective in situ endoscopic diagnosis and treatment, size measurement and shape characterization of lesions, such as tumors, is important. For this purpose, in the past we have developed a range of 3D endoscopic systems based on active stereo to measure the shape and size of living tissues. In those works, the main shortcoming was that the target area could only be reconstructed at a specific...
In this paper we investigate a method for segmentation of colorectal Narrow Band Imaging (NBI) endoscopic images with Support Vector Machine (SVM) and Markov Random Field (MRF). SVM classifiers recognize each square patch of an NBI image and output posterior probabilities that represent how likely the given patch falls into a certain label. To prevent the spatial inconsistency between adjacent patches...
In this paper, we propose a self-training method which uses unlabeled regions in the original images obtained from a colorectal Narrow Band Imaging (NBI) zoom-videoendoscope. The proposed method first trims a number of patches from unlabeled regions in the original images and uses them as unlabeled training samples. Classifiers are trained with the available labeled samples, as well as with those...
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