keyword
https://read.qxmd.com/read/36653977/iterative-ct-reconstruction-based-on-admm-using-shearlet-sparse-regularization
#1
JOURNAL ARTICLE
Dayu Xiao, Jianhua Li, Ruotong Zhao, Shouliang Qi, Yan Kang
The total variation (TV) method favors solutions with the piece-wise constant assumption of the desired image from sparse-view sampling, for example, simple geometric images with flat intensity. When the phantoms become more complex and contain complicated textures, for example, high-resolution phantom and lung CT images, the images reconstructed by TV regularization may lose their contrast and fine structures. One of the optimally sparse transforms for images, the shearlet transform, has C2 without discontinuities on C2 curves, giving excellent sensitive directional information as compared with other wavelet transform approaches...
August 16, 2022: Mathematical Biosciences and Engineering: MBE
https://read.qxmd.com/read/35898680/image-super-resolution-reconstruction-method-for-lung-cancer-ct-scanned-images-based-on-neural-network
#2
JOURNAL ARTICLE
Jianming Xu, Weichun Liu, Yang Qin, Guangrong Xu
The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer...
2022: BioMed Research International
https://read.qxmd.com/read/33953285/regional-and-organ-level-responses-to-local-lung-irradiation-in-sheep
#3
JOURNAL ARTICLE
David Collie, Steven H Wright, Jorge Del-Pozo, Elaine Kay, Tobias Schwarz, Magdalena Parys, Jessica Lawrence
Lung is a dose-limiting organ in radiotherapy. This may limit tumour control when effort is made in planning to limit the likelihood of radiation-induced lung injury (RILI). Understanding the factors that dictate susceptibility to radiation-induced pulmonary fibrosis will aid in the prevention and management of RILI, and may lead to more effective personalized radiotherapy treatment. As the interaction of regional and organ-level responses may shape the chronic consequences of RILI, we sought to characterise both aspects of the response in an ovine model...
May 5, 2021: Scientific Reports
https://read.qxmd.com/read/31526257/bone-marrow-and-tumor-radiomics-at-18-f-fdg-pet-ct-impact-on-outcome-prediction-in-non-small-cell-lung-cancer
#4
JOURNAL ARTICLE
Sarah A Mattonen, Guido A Davidzon, Jalen Benson, Ann N C Leung, Minal Vasanawala, George Horng, Joseph B Shrager, Sandy Napel, Viswam S Nair
Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016...
November 2019: Radiology
https://read.qxmd.com/read/30854452/-18f-fdg-positron-emission-tomography-pet-tumor-and-penumbra-imaging-features-predict-recurrence-in-non-small-cell-lung-cancer
#5
JOURNAL ARTICLE
Sarah A Mattonen, Guido A Davidzon, Shaimaa Bakr, Sebastian Echegaray, Ann N C Leung, Minal Vasanawala, George Horng, Sandy Napel, Viswam S Nair
We identified computational imaging features on 18F-fluorodeoxyglucose positron emission tomography (PET) that predict recurrence/progression in non-small cell lung cancer (NSCLC). We retrospectively identified 291 patients with NSCLC from 2 prospectively acquired cohorts (training, n = 145; validation, n = 146). We contoured the metabolic tumor volume (MTV) on all pretreatment PET images and added a 3-dimensional penumbra region that extended outward 1 cm from the tumor surface. We generated 512 radiomics features, selected 435 features based on robustness to contour variations, and then applied randomized sparse regression (LASSO) to identify features that predicted time to recurrence in the training cohort...
March 2019: Tomography: a Journal for Imaging Research
https://read.qxmd.com/read/28268558/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#6
JOURNAL ARTICLE
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries...
August 2016: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/28226735/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#7
JOURNAL ARTICLE
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Xinyang Feng, Jie Yang, Elsa D Angelini, Andrew F Laine
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/27740476/a-3-d-riesz-covariance-texture-model-for-prediction-of-nodule-recurrence-in-lung-ct
#8
Pol Cirujeda, Yashin Dicente Cid, Henning Muller, Daniel Rubin, Todd A Aguilera, Billy W Loo, Maximilian Diehn, Xavier Binefa, Adrien Depeursinge
This paper proposes a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter- variations of the feature space dimensions. When compared to the classical use of the average for feature aggregation, feature covariances preserve spatial co-variations between features...
July 18, 2016: IEEE Transactions on Medical Imaging
https://read.qxmd.com/read/27429433/a-3-d-riesz-covariance-texture-model-for-prediction-of-nodule-recurrence-in-lung-ct
#9
JOURNAL ARTICLE
Pol Cirujeda, Yashin Dicente Cid, Henning Muller, Daniel Rubin, Todd A Aguilera, Billy W Loo, Maximilian Diehn, Xavier Binefa, Adrien Depeursinge
This paper proposes a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter-variations of the feature space dimensions. When compared to the classical use of the average for feature aggregation, feature covariances preserve spatial co-variations between features...
December 2016: IEEE Transactions on Medical Imaging
https://read.qxmd.com/read/24886031/automatic-pulmonary-fissure-detection-and-lobe-segmentation-in-ct-chest-images
#10
JOURNAL ARTICLE
Shouliang Qi, Han J W van Triest, Yong Yue, Mingjie Xu, Yan Kang
BACKGROUND: Multi-detector Computed Tomography has become an invaluable tool for the diagnosis of chronic respiratory diseases. Based on CT images, the automatic algorithm to detect the fissures and divide the lung into five lobes will help regionally quantify, amongst others, the lung density, texture, airway and, blood vessel structures, ventilation and perfusion. METHODS: Sagittal adaptive fissure scanning based on the sparseness of the vessels and bronchi is employed to localize the potential fissure region...
May 7, 2014: Biomedical Engineering Online
https://read.qxmd.com/read/24595347/total-variation-stokes-strategy-for-sparse-view-x-ray-ct-image-reconstruction
#11
JOURNAL ARTICLE
Yan Liu, Zhengrong Liang, Jianhua Ma, Hongbing Lu, Ke Wang, Hao Zhang, William Moore
Previous studies have shown that by minimizing the total variation (TV) of the to-be-estimated image with some data and/or other constraints, a piecewise-smooth X-ray computed tomography image can be reconstructed from sparse-view projection data. However, due to the piecewise constant assumption for the TV model, the reconstructed images are frequently reported to suffer from the blocky or patchy artifacts. To eliminate this drawback, we present a total variation-stokes-projection onto convex sets (TVS-POCS) reconstruction method in this paper...
March 2014: IEEE Transactions on Medical Imaging
https://read.qxmd.com/read/23674412/multimodal-sparse-representation-based-classification-for-lung-needle-biopsy-images
#12
JOURNAL ARTICLE
Yinghuan Shi, Yang Gao, Yubin Yang, Ying Zhang, Dong Wang
Lung needle biopsy image classification is a critical task for computer-aided lung cancer diagnosis. In this study, a novel method, multimodal sparse representation-based classification (mSRC), is proposed for classifying lung needle biopsy images. In the data acquisition procedure of our method, the cell nuclei are automatically segmented from the images captured by needle biopsy specimens. Then, features of three modalities (shape, color, and texture) are extracted from the segmented cell nuclei. After this procedure, mSRC goes through a training phase and a testing phase...
October 2013: IEEE Transactions on Bio-medical Engineering
https://read.qxmd.com/read/23340591/feature-based-image-patch-approximation-for-lung-tissue-classification
#13
JOURNAL ARTICLE
Yang Song, Weidong Cai, Yun Zhou, David Dagan Feng
In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches...
April 2013: IEEE Transactions on Medical Imaging
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