ArticleMedical physics2012
Cupping artifact correction and automated classification for high-resolution dedicated breast CT images.
Article in Medical physics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Cone-beam breast CT using an offset detector: effect of detector offset and image reconstruction algorithm.Physics in medicine and biology · 2022Article
- Flat-panel conebeam CT in the clinic: history and current state.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy.Frontiers in oncology · 2021Article
- Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach.Medical physics · 2019Article
- Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging.International journal of computer assisted radiology and surgery · 2018Article
- Internal breast dosimetry in mammography: Monte Carlo validation in homogeneous and anthropomorphic breast phantoms with a clinical mammography system.Medical physics · 2018Article
- PSNet: prostate segmentation on MRI based on a convolutional neural network.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- A Denoising Algorithm for CT Image Using Low-rank Sparse Coding.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- High-resolution CT Image Retrieval Using Sparse Convolutional Neural Network.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Improving Image Quality of Cone-Beam CT Using Alternating Regression Forest.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- A Monte Carlo model for mean glandular dose evaluation in spot compression mammography.Medical physics · 2017Article
- X-ray scatter correction for dedicated cone beam breast CT using a forward-projection model.Medical physics · 2017Article
- A Patch-based CBCT Scatter Artifact Correction Using Prior CT.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- A supervoxel-based segmentation method for prostate MR images.Medical physics · 2017Article
- Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- Deep Learning based Classification for Head and Neck Cancer Detection with Hyperspectral Imaging in an Animal Model.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- Dosimetry in x-ray-based breast imaging.Physics in medicine and biology · 2016Article
- Library based x-ray scatter correction for dedicated cone beam breast CT.Medical physics · 2016Article
- An energy minimization method for the correction of cupping artifacts in cone-beam CT.Journal of applied clinical medical physics · 2016Article
- A general method for cupping artifact correction of cone-beam breast computed tomography images.International journal of computer assisted radiology and surgery · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeTo develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images.
methodsThe original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images.
resultsThe correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% ± 2.0%.
conclusionsA 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.
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