TY - GEN
T1 - Automatic collimation detection in digital radiographs with the directed hough transform and learning-based edge detection
AU - Zhao, Liang
AU - Peng, Zhigang
AU - Finkler, Klaus
AU - Jerebko, Anna
AU - Corso, Jason J.
AU - Zhou, Xiang Sean
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Collimation is widely used for X-ray examinations to reduce the overall radiation exposure to the patient and improve the contrast resolution in the region of interest (ROI), that has been exposed directly to X-rays. It is desirable to detect the region of interest and exclude the unexposed area to optimize the image display. Although we only focus on the X-ray images generated with a rectangular collimator, it remains a challenging task because of the large variability of collimated images. In this study, we detect the region of interest as an optimal quadrilateral, which is the intersection of the optimal group of four half-planes. Each half-plane is defined as the positive side of a directed straight line. We develop an extended Hough transform for directed straight lines on a model-aware gray level edge-map, which is estimated with random forests [1] on features of pairs of superpixels. Experiments show that our algorithm can extract the region of interest quickly and accurately, despite variations in size, shape and orientation, and incompleteness of boundaries.
AB - Collimation is widely used for X-ray examinations to reduce the overall radiation exposure to the patient and improve the contrast resolution in the region of interest (ROI), that has been exposed directly to X-rays. It is desirable to detect the region of interest and exclude the unexposed area to optimize the image display. Although we only focus on the X-ray images generated with a rectangular collimator, it remains a challenging task because of the large variability of collimated images. In this study, we detect the region of interest as an optimal quadrilateral, which is the intersection of the optimal group of four half-planes. Each half-plane is defined as the positive side of a directed straight line. We develop an extended Hough transform for directed straight lines on a model-aware gray level edge-map, which is estimated with random forests [1] on features of pairs of superpixels. Experiments show that our algorithm can extract the region of interest quickly and accurately, despite variations in size, shape and orientation, and incompleteness of boundaries.
UR - http://www.scopus.com/inward/record.url?scp=84955307428&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84955307428&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-28194-0_9
DO - 10.1007/978-3-319-28194-0_9
M3 - Conference contribution
AN - SCOPUS:84955307428
SN - 9783319281933
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 71
EP - 78
BT - Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers
A2 - Coupé, Pierrick
A2 - Munsell, Brent
A2 - Wu, Guorong
A2 - Zhan, Yiqiang
A2 - Rueckert, Daniel
T2 - 1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015
Y2 - 9 October 2015 through 9 October 2015
ER -