Semantics and CBIR: a medical imaging perspective
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Search task performance using subtle gaze direction with the presence of distractions
ACM Transactions on Applied Perception (TAP)
Fast and Robust 3-D MRI Brain Structure Segmentation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Hierarchical detection of multiple organs using boosted features
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
A probabilistic model for haustral curvatures with applications to colon CAD
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Automatic detection and segmentation of axillary lymph nodes
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
MICCAI'11 Proceedings of the 2011 international conference on Prostate cancer imaging: image analysis and image-guided interventions
A cascade learning method for liver lesion detection in CT images
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Computer Vision and Image Understanding
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Automatic polyp detection is an increasingly important task in medical imaging with virtual colonoscopy [15] being widely used. In this paper, we present a 3D object detection algorithm and show its application on polyp detection from CT images. We make the following contributions: (1) The system adopts Probabilistic Boosting Tree (PBT) to probabilistically detect polyps. Integral volume and 3D Haar filters are introduced to achieve fast feature computation. (2) We give an explicit convergence rate analysis for the AdaBoost algorithm [2] and prove that the error at each step \in t+1. is tightly bounded by the previous error \in t. (3) For a 3D polyp template, a generative model is defined. Given the bound and convergence analysis, we analyze the role of "sample alignment" in the template design and devise a robust and efficient algorithm for polyp detection. The overall system has been tested on 150 volumes and the results obtained are very encouraging.