Query: shading correction

title: Fast shading correction for cone beam CT via partitioned tissue classification.
abstract: The quantitative use of cone beam computed tomography (CBCT) in radiation therapy is limited by severe shading artifacts, even with system embedded correction. We recently proposed effective shading correction methods, using planning CT (pCT) as prior information to estimate low frequency errors in either the projection domain or image domain. In this work, we further improve the clinical practicality of our previous methods by removing the requirement of prior pCT images. Clinical CBCT images are typically composed of a limited number of tissues. By utilizing the low frequency characteristic of shading distribution, we first generate a 'shading free' template image by enforcing uniformity on CBCT voxels of the same tissue type via a technique named partitioned tissue classification. Only a small subset of voxels in the template image are used in the correction process to generate sparse samples of shading artifacts. Local filtration, a Fourier transform based algorithm, is employed to efficiently process the sparse errors to compute a full field distribution of shading artifacts for CBCT correction. We evaluate the method's performance using an anthropomorphic pelvis phantom and 6 pelvis patients. The proposed method improves the image quality of CBCT for both phantom and patients to a level matching that of pCT. On the pelvis phantom, the signal non uniformity (SNU) is reduced from 12.11% to 3.11% and 8.40% to 2.21% on fat and muscle, respectively. The maximum CT number error is reduced from 70 to 10 HU and 73 to 11 HU on fat and muscle, respectively. On patients, the average SNU is reduced from 9.22% to 1.06% and 11.41% to 1.67% on fat and muscle, respectively. The maximum CT number error is reduced from 95 to 9 HU and 88 to 8 HU on fat and muscle, respectively. The typical processing time for one CBCT dataset is about 45 s on a standard PC.

title: Determining the shading correction factor using a smartphone camera with a fisheye lens
abstract: Quasi steady state methods to calculate monthly heating and cooling energy needs require the quantification of solar heat gains through windows. The presence of external obstacles, such as natural topography, other buildings, overhangs, or side fins, may significantly decrease solar gains. Simplified methods to evaluate shading on windows throughout the year use the shading correction factor concept, i.e. the ratio between the surface solar irradiation in the presence of external obstacles and in their absence. This paper presents a photographic method using a low cost fisheye lens attached to a smartphone to quantify the shading correction factor. It is based on image processing, solar geometry, and climate databases and does not require 3D building design or angle measurement of external obstacles. Both beam and diffuse correction factors are calculated monthly. The model correctly predicts the real Sun's position in the photos and the expected monthly shading of a South window with an overhang. For the diffuse correction factor, errors up to 11% are obtained when compared to the analytical solution of a window with a large overhang. Standard tabulated values for windows with multiple obstructions are conservative when compared to those of the photographic method. The tabulated approach considers the effect of various obstacles shading even when overlapping occurs, which possibly explains the difference between methods.

title: Shading correction for volumetric CT using deep convolutional neural network and adaptive filter.
abstract: Shading artifact may lead to CT number inaccuracy, image contrast loss and spatial non uniformity (SNU) which is considered as one of the fundamental limitations for volumetric CT (VCT) application. To correct the shading artifact, a novel approach is proposed using deep learning and an adaptive filter (AF) Methods Firstly, we apply the deep convolutional neural network (DCNN) to train a human tissue segmentation model. The trained model is implemented to segment the tissue. According to the general knowledge that CT number of the same human tissue is approximately the same, a template image without shading artifact can be generated using segmentation and then each tissue is filled with the corresponding CT number of a specific tissue. By subtracting the template image from the uncorrected image, the residual image with image detail and shading artifact are generated. The shading artifact is mainly low frequency signals while the image details are mainly high frequency signals. Therefore, we proposed an adaptive filter to separate the shading artifact and image details accurately. Finally, the estimated shading artifacts are deleted from the raw image to generate the corrected image.  On the Catphan(c)504 study, the error of CT number in the corrected image's region of interest (ROI) is reduced from 109 to 11 HU, and the image contrast is increased by a factor of 1.46 on average. On the patient pelvis study, the error of CT number in selected ROI is reduced from 198 to 10 HU. The SNU calculated from the ROIs decreases from 24% to 9% after correction.  The proposed shading correction method using DCNN and AF may find a useful application in future clinical practice.

title: Comparison of Selected Shading Correction Methods
abstract: Shade effect is a defect of the images very often invisible for human vision perception but may cause difficulties in proper image processing and object detection especially if the aim of the task is to proceed detection and quantitative analysis of the objects. There are several methods in image processing systems or presented in the literature, however some of them introduce unexpected changes in the images, what may interfere the final quantitative analysis. In order to solve this problem, authors proposed a new method for shade correction, which is based on simulation of the image background based on analytical methods which return pixel values representing smooth grey level changes. Comparison of the effects of correction by applying standard methods and the method proposed are presented.

title: Shading Correction with Thresholding
abstract: Some photographs are not homogeneously light because the photographed object was not homogeneously illuminated. Fine details in the dark area of such photographs are hardly recognizable. We suggest a method to improve the quality of such images.

title: A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images
abstract: CBCT images suffer from acute shading artifacts primarily due to scatter. Numerous image domain correction algorithms have been proposed in the literature that use patient specific planning CT images to estimate shading contributions in CBCT images. However, in the context of radiosurgery applications such as gamma knife, planning images are often acquired through MRI which impedes the use of polynomial fitting approaches for shading correction. We present a new shading correction approach that is independent of planning CT images. Our algorithm is based on the assumption that true CBCT images follow a uniform volumetric intensity distribution per material, and scatter perturbs this uniform texture by contributing cupping and shading artifacts in the image domain. The framework is a combination of fuzzy C means coupled with a neighborhood regularization term and Otsu's method. Experimental results on artificially simulated craniofacial CBCT images are provided to demonstrate the effectiveness of our algorithm. Spatial non uniformity is reduced from 16% to 7% in soft tissue and from 44% to 8% in bone regions. With shading correction, thresholding based segmentation accuracy for bone pixels is improved from 85% to 91% when compared to thresholding without shading correction. The proposed algorithm is thus practical and qualifies as a plug and play extension into any CBCT reconstruction software for shading correction.

title: A BaSiC tool for background and shading correction of optical microscopy images
abstract: Quantitative analysis of bioimaging data is often skewed by both shading in space and background variation in time. We introduce BaSiC, an image correction method based on low rank and sparse decomposition which solves both issues. In comparison to existing shading correction tools, BaSiC achieves high accuracy with significantly fewer input images, works for diverse imaging conditions and is robust against artefacts. Moreover, it can correct temporal drift in time lapse microscopy data and thus improve continuous single cell quantification. BaSiC requires no manual parameter setting and is available as a Fiji/ImageJ plugin.

title: 3D model based shading correction for the enhancement of multispectral colour measurement accuracy

title: Fast shading correction for cone beam CT in radiation therapy via sparse sampling on planning CT
abstract: The image quality of cone beam computed tomography (CBCT) is limited by severe shading artifacts, hindering its quantitative applications in radiation therapy. In this work, we propose an image domain shading correction method using planning CT (pCT) as prior information which is highly adaptive to clinical environment.  We propose to perform shading correction via sparse sampling on pCT. The method starts with a coarse mapping between the first pass CBCT images obtained from the Varian TrueBeam system and the pCT. The scatter correction method embedded in the Varian commercial software removes some image errors but the CBCT images still contain severe shading artifacts. The difference images between the mapped pCT and the CBCT are considered as shading errors, but only sparse shading samples are selected for correction using empirical constraints to avoid carrying over false information from pCT. A Fourier Transform based technique, referred to as local filtration, is proposed to efficiently process the sparse data for effective shading correction. The performance of the proposed method is evaluated on one anthropomorphic pelvis phantom and 17 patients, who were scheduled for radiation therapy. (The codes of the proposed method and sample data can be downloaded from https:/sites.google.com/view/linxicbct)  The proposed shading correction substantially improves the CBCT image quality on both the phantom and the patients to a level close to that of the pCT images. On the phantom, the spatial nonuniformity (SNU) difference between CBCT and pCT is reduced from 74 to 1 HU. The root of mean square difference of SNU between CBCT and pCT is reduced from 83 to 10 HU on the pelvis patients, and from 101 to 12 HU on the thorax patients. The robustness of the proposed shading correction is fully investigated with simulated registration errors between CBCT and pCT on the phantom and mis registration on patients. The sparse sampling scheme of our method successfully avoids false structures in the corrected CBCT even when the maximum registration error is as high as 8 mm.  We develop an effective shading correction algorithm for CBCT readily implementable on clinical data as a software plug in without modifications of current imaging hardware and protocol. The algorithm is directly applied on the output images from a commercial CBCT scanner with high computational efficiency and negligible memory burden.

title: Shading correction assisted iterative cone beam CT reconstruction.
abstract: Recent advances in total variation (TV) technology enable accurate CT image reconstruction from highly under sampled and noisy projection data. The standard iterative reconstruction algorithms, which work well in conventional CT imaging, fail to perform as expected in cone beam CT (CBCT) applications, wherein the non ideal physics issues, including scatter and beam hardening, are more severe. These physics issues result in large areas of shading artifacts and cause deterioration to the piecewise constant property assumed in reconstructed images. To overcome this obstacle, we incorporate a shading correction scheme into low dose CBCT reconstruction and propose a clinically acceptable and stable three dimensional iterative reconstruction method that is referred to as the shading correction assisted iterative reconstruction. In the proposed method, we modify the TV regularization term by adding a shading compensation image to the reconstructed image to compensate for the shading artifacts while leaving the data fidelity term intact. This compensation image is generated empirically, using image segmentation and low pass filtering, and updated in the iterative process whenever necessary. When the compensation image is determined, the objective function is minimized using the fast iterative shrinkage thresholding algorithm accelerated on a graphic processing unit. The proposed method is evaluated using CBCT projection data of the Catphan(c) 600 phantom and two pelvis patients. Compared with the iterative reconstruction without shading correction, the proposed method reduces the overall CT number error from around 200 HU to be around 25 HU and increases the spatial uniformity by a factor of 20 percent, given the same number of sparsely sampled projections. A clinically acceptable and stable iterative reconstruction algorithm for CBCT is proposed in this paper. Differing from the existing algorithms, this algorithm incorporates a shading correction scheme into the low dose CBCT reconstruction and achieves more stable optimization path and more clinically acceptable reconstructed image. The method proposed by us does not rely on prior information and thus is practically attractive to the applications of low dose CBCT imaging in the clinic.