Since the ideal image is difficult to be stripped from the actual sampling, this study aims to address and test a shared noise which as the knowledge and is included in the signal applying to the sampled projection to generate high qualified X-ray imaging by reducing the artifacts in computed tomography (CT). Combined with the randomness of the noise, the prereconstruction of the original projection is performed first, and then the forward projection which contains the equivalent noise in the image is obtained. Based on the slice updating, the forward and reconstruction processing is employed again. By means of threshold setting, multiple forward projections are accumulated, whereas the noise upon them will be reduced by averaging process. The noise is suppressed, and the expected information emerges, simultaneously. Study results show effective results, and the proposed method is practical and attractive as a preferred solution to CT artifacts suppression. It provides reliable guarantee for the CT inspection of internal and external dimensions.
In cone-beam computed tomography (CBCT) systems based on flat-panel detector imaging, the presence of scatter significantly reduces the quality of slices. Based on the concept of collimation, this paper presents a scatter measurement and correction method based on single grating scan. First, according to the characteristics of CBCT imaging, the scan method using single grating and the design requirements of the grating are analyzed and figured out. Second, by analyzing the composition of object projection images and object-and-grating projection images, the processing method for the scatter image at single projection angle is proposed. In addition, to avoid additional scan, this paper proposes an angle interpolation method of scatter images to reduce scan cost. Finally, the experimental results show that the scatter images obtained by this method are accurate and reliable, and the effect of scatter correction is obvious. When the additional object-and-grating projection images are collected and interpolated at intervals of 30 deg, the scatter correction error of slices can still be controlled within 3%.
Projection matrix is an essential and time-consuming part in computed tomography (CT) iterative reconstruction. In this article a novel calculation algorithm of three-dimensional (3D) projection matrix is proposed to quickly acquire the matrix for cone-beam CT (CBCT). The CT data needed to be reconstructed is considered as consisting of the three orthogonal sets of equally spaced and parallel planes, rather than the individual voxels. After getting the intersections the rays with the surfaces of the voxels, the coordinate points and vertex is compared to obtain the index value that the ray traversed. Without considering ray-slope to voxel, it just need comparing the position of two points. Finally, the computer simulation is used to verify the effectiveness of the algorithm.
An adaptive windowed range-constrained Otsu method using local information is proposed for improving the performance of image segmentation. First, the reason why traditional thresholding methods do not perform well in the segmentation of complicated images is analyzed. Therein, the influences of global and local thresholdings on the image segmentation are compared. Second, two methods that can adaptively change the size of the local window according to local information are proposed by us. The characteristics of the proposed methods are analyzed. Thereby, the information on the number of edge pixels in the local window of the binarized variance image is employed to adaptively change the local window size. Finally, the superiority of the proposed method over other methods such as the range-constrained Otsu, the active contour model, the double Otsu, the Bradley’s, and the distance-regularized level set evolution is demonstrated. It is validated by the experiments that the proposed method can keep more details and acquire much more satisfying area overlap measure as compared with the other conventional methods.
The broadening application of cone-beam Computed Tomography (CBCT) in medical diagnostics and nondestructive testing, necessitates advanced denoising algorithms for its 3D images. The block-matching and four dimensional filtering algorithm with adaptive variance (BM4D-AV) is applied to the 3D image denoising in this research. To optimize it, the key filtering parameters of the BM4D-AV algorithm are assessed firstly based on the simulated CBCT images and a table of optimized filtering parameters is obtained. Then, considering the complexity of the noise in realistic CBCT images, possible noise standard deviations in BM4D-AV are evaluated to attain the chosen principle for the realistic denoising. The results of corresponding experiments demonstrate that the BM4D-AV algorithm with optimized parameters presents excellent denosing effect on the realistic 3D CBCT images.
Flat panel detector (FPD) has been widely used as the imaging unit in the current X-ray digital radiography (DR)
systems and Computed Tomography (CT) systems. Point spread function (PSF) is an important indicator of the FPD
imaging system, but also the basis for image restoration. For the problem of poor accuracy of the FPD’s PSF
measurement with the original pinhole imaging for DR systems, a new PSF measuring method with the pinhole imaging based on the image restoration is proposed in this paper. Firstly, some images collected with the pinhole imaging are averaged to one image to reducing the noise. Then, the original pinhole image is calculated according to the energy conservation principle of point spread. Finally, the PSF of the FPD is obtained using the operation of image restoration. On this basis, through the fitting of the characteristic parameters of the PSF on different scan conditions, the computational model of the PSF is established for any scan conditions. Experimental results show that the method can obtain a more accurate PSF of the FPD, and the PSF of the same system under any scan conditions can be directly calculated with the PSF model.
On the basis of studying Non-Local Means (NLM) denoising algorithm and its pixel-wise processing algorithm in
Graphics Processing Unit (GPU), a whole image accumulation algorithm of NLM denoising algorithm based on GPU is
proposed. The number of dynamic instructions of fragment shader is effectively reduced by redesigning the data
structure and processing flow, that make the algorithm suitable to the graphic cards supported Shader Model 3.0 and/or
Shader Model 4.0, and so enhance the versatility of the algorithm. Then the continuous and parallel processing method
for 4 gray images based on Multiple Render Target (MRT) and double Frame Buffer Object (FBO) is proposed, and the
whole processing flow with GPU is presented. The experimental results of both simulative and practical gray images
show that the proposed method can achieve a speedup of 45 times while remaining the same accuracy.
Aiming at the artifact corrections for batch scans in Cone-Beam Computed Tomography (CBCT) system, the concept of
detection model is proposed. Expressing the prior knowledge of CT system and scanned object properties by the
detection model, the rapid artifact corrections are achieved based on the object knowledge, which can save the machine
consumption, enhance the detection efficiency and improve the correction effect. Based on the Digital Radiography
(DR) imaging conditions remained basically unchanged in the batch scans, the modeling method of detection model is
established by getting the relevant information through the detected scanning for one of a batch of parts. Finally, the
processing flow of CBCT scans and artifact corrections of a batch of parts based on the detection model is given, and
some key problems in the flow are discussed to improve the practical operability of the method. The experimental result
shows that the modeling method of detection model is feasible, and the rapid CBCT scans and effective artifact
corrections can be realized based on the obtained detection model.
According to the particularities of Cone-Beam Computed Tomography (CBCT) slice images, such as much noise information, low contrast, and variational region gray distribution, a defect segmentation algorithm is proposed, in which the topological regions of the whole image are segmented before segmenting the defects in each topological regions. Firstly, the gray topological structures in the slice image are obtained by image denoising pretreatment with mixture filter, iterative multi-threshold segmentation for the whole image, and extracting topological connective regions from the segmentation result. Then, region growing algorithm is applied into the defect detection in each topological structure. By this way, the low contrast segmentation in the whole image is translated into the higher contrast segmentation in the local topological structures to improving the precision and accuracy of the defect segmentation. Finally, the all extracted defects are identified by removing the "false defects" to obtain the real defects. This algorithm is used to processing the CBCT slice image of wax model of hollow turbine blade. The experiment result shows that the defects in the slice image are extracted effectively. The algorithm can be applied to the image segmentation processing which has similar characteristics with CBCT slice images.