Robust Real-Time Face Detection
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Segmentation of medical images using a genetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Distributed Markovian segmentation: Application to MR brain scans
Pattern Recognition
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic segmentation of intra-treatment CT images for adaptive radiation therapy of the prostate
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IEEE Transactions on Image Processing
Computer Methods and Programs in Biomedicine
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Landmark detection in cardiac MRI using learned local image statistics
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Segmentation of prostate is highly important in the external beam radiotherapy of prostate cancer. However, it is challenging to localize prostate in the CT images due to low image contrast, prostate motion, and both intensity and shape changes of bladder and rectum around the prostate. In this paper, an online learning and patient-specific classification method based on locationadaptive image context is proposed to precisely segment prostate in the CT image. Specifically, two sets of position-adaptive classifiers are respectively placed along the two coordinate directions, and further trained with the previous segmented treatment images to jointly perform the prostate segmentation. In particular, each location-adaptive classifier is recursively trained with different image context collected at different scales and orientations for better identification of each prostate region. The proposed learning-based prostate segmentation method has been extensively evaluated on a large set of patients, achieving very promising results.