Portal film enhancement: Technique and clinical utility

Rosenman, Julian G.; Roe, Cheryl; Cromartie, Robert; Muller, Keith E.; Pizer, Stephen M.

doi:10.1016/0360-3016(93)90357-2

Cited by 44 publications

(28 citation statements)

References 16 publications

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“…Human visual perception is known to be affected the most by contrast changes 16, 17, 24. In this study, CLAHE algorithm was used for contrast enhancement 16, 21, 23.…”

Section: Methodsmentioning

confidence: 99%

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Enhancement of megavoltage electronic portal images for markerless tumor tracking

Cheong

Yoon

Park

et al. 2018

J Applied Clin Med Phys

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PurposeThe 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.MethodsImages 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.ResultsDeblurring 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.ConclusionsConsidering 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.

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“…Human visual perception is known to be affected the most by contrast changes 16, 17, 24. In this study, CLAHE algorithm was used for contrast enhancement 16, 21, 23.…”

Section: Methodsmentioning

confidence: 99%

“…Image enhancements have been used to obtain the optimal readability of EPID images, including contrast improvement, edge deblurring, and noise reduction 16, 17, 18. A sequential application of these three image processing protocols was also proposed 18.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of megavoltage electronic portal images for markerless tumor tracking

Cheong

Yoon

Park

et al. 2018

J Applied Clin Med Phys

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“…~ Tbere is some reason to believe that digital mammography rnight allow for greater contrast and improved detection of small and early tumors over standard film screen technology, especially ir image processing is used to improve image contrast. 2,3 There are many potentially useful image processing algorithms, and each algorithm has a number of parameters that can be systematically varied to improve or worsen lesion detectability. Radiologists cannot and should not evaluate these algorithms in the clinic with real patients.…”

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confidence: 99%

Does intensity windowing improve the detection of simulated calcifications in dense mammograms?

et al. 1997

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This study attempts to determine whether intensity windowing (IW) improves detection of simulated calcifications in dense mammograms. Clusters of five simulated calcifications were embedded in dense mammograms digitized at 50-1~m pixels, 12 bits deep. Film images with no windowing applied were compared with film images with nine different window widths and levels applied. A simulated cluster was embedded in a realistic background of dense breast tissue, with the position of the cluster varied. The key variables involved in each trial included the position of the cluster, contrast level of the cluster, and the IW settings applied to the image. Combining the ten IW conditions, four contrast levels and four quadrant positions gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 160 backgrounds. The entire experiment consisted of 800 trials. Twenty student observers were asked to detect the quadrant of the image in which the mass was Iocated. There was a statistically significant improvement in detection performance for clusters of calcifications when the window width was set at 1024 with a level of 3328, and when the window width was set at 1024 with a level cf 3456. The selected IW settings should be tested in the clinic with digital mammograms to determine whether calcification detection performance can be improved. Copyright 9 1997 by W.B. Saunders CompanyKEY WORDS: mammography, image processing, intensity windowing, observer studies, calcifications, computers, radiology.M AMMOGRAPHY, especially in women with dense breasts, is not perfectly sensitive to all cancers. Approximately 10% to 15% of palpable malignancies are not visible mammographically. ~ Tbere is some reason to believe that digital mammography rnight allow for greater contrast and improved detection of small and early tumors over standard film screen technology, especially ir image processing is used to improve image contrast. 2,3There are many potentially useful image processing algorithms, and each algorithm has a number of parameters that can be systematically varied to improve or worsen lesion detectability. Radiologists cannot and should not evaluate these algorithms in the clinic with real patients. Such a task would be overwhelming and potentially could cause much unnecessary patient anxiety. Ideally, a test set of image phantorns with simulated lesions in known locations should be used to test each potentially useful algorithm and its attendant parameters in the laboratory setting before any patient's images ate interpreted using these algorithms. We have developed such a laboratory method for evaluation of image processing algorithms. 4 In previous work, we have shown that detection performance with the application of contrast limited adaptive equalization (CLAHE) to digitized mammograms is parallel for radiologists and student observers. 4 Using the same experimental paradigm, we report here on whether intensity windowing (IW) can improve the detection of calcifications in dense mammograms in...

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“…Histogram equalization10, 11 (HE) with or without adaptive is a relatively simple image processing method to stretch the histogram of the image intensity evenly according to pixel intensity probability 12, 13. However, HE is not able to avoid high peaks (i.e., clusters of image intensity) in the histogram; therefore cannot enhance the contrast between pixels with the peaks, i.e., within a small range of image intensity.…”

Section: Introductionmentioning

confidence: 99%

“…However, HE is not able to avoid high peaks (i.e., clusters of image intensity) in the histogram; therefore cannot enhance the contrast between pixels with the peaks, i.e., within a small range of image intensity. The contrast limited adaptive histogram equalization (CLAHE) algorithm11, 14 has been developed to overcome such limitations by processing the image histogram in blocks, limiting the intensity dynamic range,15 and then clipping and redistributing the gray peaks 14, 16. CLAHE has been applied to a variety of medical images17, 18, 19, 20, 21 including mammogram,22 digital radiology,23 and entropy 24.…”

Section: Introductionmentioning

confidence: 99%

Automatic x‐ray image contrast enhancement based on parameter auto‐optimization

Qiu

Zhang

et al. 2017

J Applied Clin Med Phys

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PurposeInsufficient image contrast associated with radiation therapy daily setup x‐ray images could negatively affect accurate patient treatment setup. We developed a method to perform automatic and user‐independent contrast enhancement on 2D kilo voltage (kV) and megavoltage (MV) x‐ray images. The goal was to provide tissue contrast optimized for each treatment site in order to support accurate patient daily treatment setup and the subsequent offline review.MethodsThe proposed method processes the 2D x‐ray images with an optimized image processing filter chain, which consists of a noise reduction filter and a high‐pass filter followed by a contrast limited adaptive histogram equalization (CLAHE) filter. The most important innovation is to optimize the image processing parameters automatically to determine the required image contrast settings per disease site and imaging modality. Three major parameters controlling the image processing chain, i.e., the Gaussian smoothing weighting factor for the high‐pass filter, the block size, and the clip limiting parameter for the CLAHE filter, were determined automatically using an interior‐point constrained optimization algorithm.ResultsFifty‐two kV and MV x‐ray images were included in this study. The results were manually evaluated and ranked with scores from 1 (worst, unacceptable) to 5 (significantly better than adequate and visually praise worthy) by physicians and physicists. The average scores for the images processed by the proposed method, the CLAHE, and the best window‐level adjustment were 3.92, 2.83, and 2.27, respectively. The percentage of the processed images received a score of 5 were 48, 29, and 18%, respectively.ConclusionThe proposed method is able to outperform the standard image contrast adjustment procedures that are currently used in the commercial clinical systems. When the proposed method is implemented in the clinical systems as an automatic image processing filter, it could be useful for allowing quicker and potentially more accurate treatment setup and facilitating the subsequent offline review and verification.

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Portal film enhancement: Technique and clinical utility

Cited by 44 publications

References 16 publications

Enhancement of megavoltage electronic portal images for markerless tumor tracking

Enhancement of megavoltage electronic portal images for markerless tumor tracking

Does intensity windowing improve the detection of simulated calcifications in dense mammograms?

Automatic x‐ray image contrast enhancement based on parameter auto‐optimization

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