Open AccessEnhancement of megavoltage electronic portal images for markerless tumor tracking
Author(s) -
Cheong KwangHo,
Yoon JaiWoong,
Park Soah,
Hwang Taejin,
Kang SeiKwon,
Koo Taeryool,
Han Tae Jin,
Kim Haeyoung,
Lee Me Yeon,
Kim Kyoung Ju,
Bae Hoonsik
Publication year - 2018
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.12411
Subject(s) - deblurring , artificial intelligence , computer vision , computer science , imaging phantom , image quality , adaptive histogram equalization , contrast (vision) , pixel , histogram , noise reduction , image processing , pattern recognition (psychology) , mathematics , histogram equalization , image restoration , nuclear medicine , image (mathematics) , medicine
Abstract Purpose The poor quality of megavoltage ( MV ) images from electronic portal imaging device ( EPID ) hinders visual verification of tumor targeting accuracy particularly during markerless tumor tracking. The aim of this study was to investigate the effect of a few representative image processing treatments on visual verification and detection capability of tumors under auto tracking. Methods Images of QC ‐3 quality phantom, a single patient's setup image, and cine images of two‐lung cancer patients were acquired. Three image processing methods were individually employed to the same original images. For each deblurring, contrast enhancement, and denoising, a total variation deconvolution, contrast‐limited adaptive histogram equalization ( CLAHE ), and median filter were adopted, respectively. To study the effect of image enhancement on tumor auto‐detection, a tumor tracking algorithm was adopted in which the tumor position was determined as the minimum point of the mean of the sum of squared pixel differences ( MSSD ) between two images. The detectability and accuracy were compared. Results Deblurring of a quality phantom image yielded sharper edges, while the contrast‐enhanced image was more readable with improved structural differentiation. Meanwhile, the denoising operation resulted in noise reduction, however, at the cost of sharpness. Based on comparison of pixel value profiles, contrast enhancement outperformed others in image perception. During the tracking experiment, only contrast enhancement resulted in tumor detection in all images using our tracking algorithm. Deblurring failed to determine the target position in two frames out of a total of 75 images. For original and denoised set, target location was not determined for the same five images. Meanwhile, deblurred image showed increased detection accuracy compared with the original set. The denoised image resulted in decreased accuracy. In the case of contrast‐improved set, the tracking accuracy was nearly maintained as that of the original image. Conclusions Considering the effect of each processing on tumor tracking and the visual perception in a limited time, contrast enhancement would be the first consideration to visually verify the tracking accuracy of tumors on MV EPID without sacrificing tumor detectability and detection accuracy.