Cupping artifact correction and automated classification for high‐resolution dedicated breast CT images

Yang, Xiaofeng; Wu, Sheng; Sechopoulos, Ioannis; Fei, Baowei

doi:10.1118/1.4754654

Cited by 49 publications

(52 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We had observed that cupping artifacts can be corrected by using reconstructed image. Similar observations had also been reported in the literature 19 , 20 …”

Section: Introductionsupporting

confidence: 92%

“…We find another cupping artifact method inspired by the MR bias correction method from the work by Yang et al (19) The results are shown in Fig. 8.…”

Section: Resultsmentioning

confidence: 99%

“…Comparison of breast CT images before (bottom left) and after (top) cupping artifact correction, using the method developed by Yang et al (19) (Bottom right) This paper's method.…”

Section: Resultsmentioning

confidence: 99%

“…In Yang et al, (19) the method automatically corrects the cupping artifacts using a nonpara‐metric coarse‐to‐fine approach which allows the cupping artifacts to be modeled with different frequency ranges and without user supervision. Figure 8 shows that our method equally for the correction of cupping artifacts, as well as that detailed in Yang et al Our method has a higher average CNR of 8.59 (Fig.…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

An energy minimization method for the correction of cupping artifacts in cone‐beam CT

Xie

Zhuang

2016

J Applied Clin Med Phys

View full text Add to dashboard Cite

The purpose of this study was to reduce cupping artifacts and improve quantitative accuracy of the images in cone‐beam CT (CBCT). An energy minimization method (EMM) is proposed to reduce cupping artifacts in reconstructed image of the CBCT. The cupping artifacts are iteratively optimized by using efficient matrix computations, which are verified to be numerically stable by matrix analysis. Moreover, the energy in our formulation is convex in each of its variables, which brings the robustness of the proposed energy minimization algorithm. The cupping artifacts are estimated as a result of minimizing this energy. The results indicate that proposed algorithm is effective for reducing the cupping artifacts and preserving the quality of the reconstructed image. The proposed method focuses on the reconstructed image without requiring any additional physical equipment; it is easily implemented and provides cupping correction using a single scan acquisition. The experimental results demonstrate that this method can successfully reduce the magnitude of cupping artifacts. The correction algorithm reported here may improve the uniformity of the reconstructed images, thus assisting the development of perfect volume visualization and threshold‐based visualization techniques for reconstructed images.PACS number(s): 87.57.cp

show abstract

“…We had observed that cupping artifacts can be corrected by using reconstructed image. Similar observations had also been reported in the literature 19 , 20 …”

Section: Introductionsupporting

confidence: 92%

“…We find another cupping artifact method inspired by the MR bias correction method from the work by Yang et al (19) The results are shown in Fig. 8.…”

Section: Resultsmentioning

confidence: 99%

“…Comparison of breast CT images before (bottom left) and after (top) cupping artifact correction, using the method developed by Yang et al (19) (Bottom right) This paper's method.…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

An energy minimization method for the correction of cupping artifacts in cone‐beam CT

Xie

Zhuang

2016

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…Several other authors have developed bCT segmentation algorithms. Notable among them are Chen, 29 Nelson et al, 30 and Yang et al 31 Chen used histogram partitioning into five subintervals followed by interval thresholding to decompose the breast into five segmentation classes based on voxel intensity. The methods of Nelson et al employed a two compartment histogram model to classify adipose, glandular, and skin tissues.…”

mentioning

confidence: 99%

Population of 224 realistic human subject-based computational breast phantoms

et al. 2015

View full text Add to dashboard Cite

Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230+ dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.

show abstract

A supervoxel‐based segmentation method for prostate MR images

et al. 2017

Self Cite

View full text Add to dashboard Cite

Purpose Segmentation of the prostate on MR images has many applications in prostate cancer management. In this work, we propose a supervoxel based segmentation method for prostate MR images. Methods A supervoxel is a set of pixels that have similar intensities, locations, and textures in a 3D image volume. The prostate segmentation problem is considered as assigning a binary label to each supervoxel, which is either the prostate or background. A supervoxel-based energy function with data and smoothness terms is used to model the label. The data term estimates the likelihood of a supervoxel belonging to the prostate by using a supervoxel-based shape feature. The geometric relationship between two neighboring supervoxels is used to build the smoothness term. The 3D graph cut is used to minimize the energy function to get the labels of the supervoxels, which yields the prostate segmentation. A 3D active contour model is then used to get a smooth surface by using the output of the graph cut as an initialization. The performance of the proposed algorithm was evaluated on 30 in-house MR image data and PROMISE12 data set. Results The mean Dice similarity coefficients are 87.2 ± 2.3% and 88.2 ± 2.8% for our 30 in-house MR volumes and the PROMISE12 data set, respectively. The proposed segmentation method yields a satisfactory result for prostate MR images. Conclusion The proposed supervoxel based method can accurately segment prostate MR images and can have a variety of application in prostate cancer diagnosis and therapy.

show abstract

Cupping artifact correction and automated classification for high‐resolution dedicated breast CT images

Abstract: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution.

Cited by 49 publications

References 56 publications

An energy minimization method for the correction of cupping artifacts in cone‐beam CT

An energy minimization method for the correction of cupping artifacts in cone‐beam CT

Population of 224 realistic human subject-based computational breast phantoms

A supervoxel‐based segmentation method for prostate MR images

Contact Info

Product

Resources

About