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Automatic on‐line electronic portal image analysis with a wavelet‐based edge detector
Author(s) -
Petrascu O.,
Bel A.,
Linthout N.,
Verellen D.,
Soete G.,
Storme G.
Publication year - 2000
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.598834
Subject(s) - imaging phantom , standard deviation , template matching , artificial intelligence , edge detection , computer science , wavelet , computer vision , image processing , mathematics , nuclear medicine , image (mathematics) , medicine , statistics
A fully automatic method for on‐line electronic portal image analysis is proposed. The method uses multiscale edge detection with wavelets for both the field outline and the anatomical structures. An algorithm to extract and combine the information from different scales has been developed. The edges from the portal image are aligned with the edges from the reference image using chamfer matching. The reference is the first portal image of each treatment. The matching is applied first to the field and subsequently to the anatomy. The setup deviations are quantified as the displacement of the anatomical structures relative to the radiation beam boundaries. The performance of the algorithm was investigated for portal images with different contrast and noise level. The automatic analysis was used first to detect simulated displacements. Then the automatic procedure was tested on anterior–posterior and lateral portal images of a pelvic phantom. In both sets of tests the differences between the measured and the actual shifts were used to quantify the performance. Finally we applied the automatic procedure to clinical images of pelvic and lung regions. The output of the procedure was compared with the results of a manual match performed by a trained operator. The errors for the phantom tests were small: average standard deviation of 0.39 mm and 0.26 degrees and absolute mean error of 0.31 mm and 0.2 degrees were obtained. In the clinical cases average standard deviations of 1.32 mm and 0.6 degrees were found. The average absolute mean errors were 1.09 mm and 0.39 degrees. Failures were registered in 2% of the phantom tests and in 3% of the clinical cases. The algorithm execution is approximately 5 s on a 168 MHz Sun Ultra 2 workstation. The automatic analysis tool is considered to be a very useful tool for on‐line setup corrections.