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https://www.readbyqxmd.com/read/29221270/analysis-of-the-clinical-differentiation-of-pulmonary-sclerosing-pneumocytoma-and-lung-cancer
#1
Jun Zhu
Background: Pulmonary sclerosing pneumocytoma (PSP) is a rare benign lung tumor. This study investigated the diagnostic experience of PSP and lung cancer. Methods: This study is a retrospective study. We observed the locations of lung lesions, imaging form and clinical symptoms, and recorded the surgical complications through comparing patients with PSP and lung cancer. Results: From December 2012 to February 2017, 187 PSP cases and 197 lung cancer cases were collected...
September 2017: Journal of Thoracic Disease
https://www.readbyqxmd.com/read/29202136/explaining-radiological-emphysema-subtypes-with-unsupervised-texture-prototypes-mesa-copd-study
#2
Jie Yang, Elsa D Angelini, Benjamin M Smith, John H M Austin, Eric A Hoffman, David A Bluemke, R Graham Barr, Andrew F Laine
Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD). Automated texture-based quantification of emphysema subtypes has been successfully implemented via supervised learning of these three emphysema subtypes. In this work, we demonstrate that unsupervised learning on a large heterogeneous database of CT scans can generate texture prototypes that are visually homogeneous and distinct, reproducible across subjects, and capable of predicting accurately the three standard radiological subtypes...
2017: Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging: MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers
https://www.readbyqxmd.com/read/29200686/an-investigation-of-bayes-algorithm-and-neural-networks-for-identifying-the-breast-cancer
#3
E Udayakumar, S Santhi, P Vetrivelan
Context: Breast cancer is a biggest threat to women. X-ray mammography is the most effective method for early detection and screening of breast cancer. It is a tough challenge for the radiologist in reading mammography since it does not provide consistent result every time. Aim: To improve the primary sign of this disease, computer-aided diagnosis schemes have been developed. Using monitor, digital images of mammography are displayed and they can be lightened or darkened before they are printed on the film...
July 2017: Indian Journal of Medical and Paediatric Oncology
https://www.readbyqxmd.com/read/29185058/content-based-image-retrieval-for-lung-nodule-classification-using-texture-features-and-learned-distance-metric
#4
Guohui Wei, Hui Cao, He Ma, Shouliang Qi, Wei Qian, Zhiqing Ma
Similarity measurement of lung nodules is a critical component in content-based image retrieval (CBIR), which can be useful in differentiating between benign and malignant lung nodules on computer tomography (CT). This paper proposes a new two-step CBIR scheme (TSCBIR) for computer-aided diagnosis of lung nodules. Two similarity metrics, semantic relevance and visual similarity, are introduced to measure the similarity of different nodules. The first step is to search for K most similar reference ROIs for each queried ROI with the semantic relevance metric...
November 29, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/29172671/pulmonary-quantitative-ct-imaging-in-focal-and-diffuse-disease-current-research-and-clinical-applications
#5
Mario Silva, Gianluca Milanese, Valeria Seletti, Alarico Ariani, Nicola Sverzellati
The frenetic development of imaging technology - both hardware and software - provides exceptional potential for investigation of the lung. In the last two decades, computed tomography (CT) was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative computed tomography (QCT). Hundreds of thousands of CTs have been analyzed for characterization of focal and diffuse disease of the lung...
November 27, 2017: British Journal of Radiology
https://www.readbyqxmd.com/read/29060752/content-based-retrieval-for-lung-nodule-diagnosis-using-learned-distance-metric
#6
Guohui Wei, He Ma, Wei Qian, Hongyang Jiang, Xinzhuo Zhao
Similarity metric of the lung nodules can be useful in differentiating between benign and malignant lung nodule lesions on computed tomography (CT). Unlike previous computerized schemes, which focus on the features extracting, we concentrate on similarity metric of the lung nodules. In this study, we first assemble a lung nodule dataset which is from LIDC-IDRI lung CT images. This dataset includes 746 lung nodules in which 375 domain radiologists identified malignant nodules and 371 domain radiologists-identified benign nodules...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29059917/a-radiomics-evaluation-of-2d-and-3d-mri-texture-features-to-classify-brain-metastases-from-lung-cancer-and-melanoma
#7
Rafael Ortiz-Ramon, Andres Larroza, Estanislao Arana, David Moratal
Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29051570/prediction-of-recurrence-in-early-stage-non-small-cell-lung-cancer-using-computer-extracted-nuclear-features-from-digital-h-e-images
#8
Xiangxue Wang, Andrew Janowczyk, Yu Zhou, Rajat Thawani, Pingfu Fu, Kurt Schalper, Vamsidhar Velcheti, Anant Madabhushi
Identification of patients with early stage non-small cell lung cancer (NSCLC) with high risk of recurrence could help identify patients who would receive additional benefit from adjuvant therapy. In this work, we present a computational histomorphometric image classifier using nuclear orientation, texture, shape, and tumor architecture to predict disease recurrence in early stage NSCLC from digitized H&E tissue microarray (TMA) slides. Using a retrospective cohort of early stage NSCLC patients (Cohort #1, n = 70), we constructed a supervised classification model involving the most predictive features associated with disease recurrence...
October 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29047033/characterization-of-pulmonary-nodules-based-on-features-of-margin-sharpness-and-texture
#9
José Raniery Ferreira, Marcelo Costa Oliveira, Paulo Mazzoncini de Azevedo-Marques
Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness...
October 18, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29036877/a-hybrid-cnn-feature-model-for-pulmonary-nodule-malignancy-risk-differentiation
#10
Huafeng Wang, Tingting Zhao, Lihong Connie Li, Haixia Pan, Wanquan Liu, Haoqi Gao, Fangfang Han, Yuehai Wang, YiFang Qi, Zhengrong Liang
The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN), which simulates human neural network for target recognition and our previously research on texture features, we present a hybrid model that takes into consideration of both global and local features for pulmonary nodule differentiation using the largest public database founded by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI)...
October 10, 2017: Journal of X-ray Science and Technology
https://www.readbyqxmd.com/read/29036692/clinical-utility-of-texture-analysis-of-18f-fdg-pet-ct-in-patients-with-stage-i-lung-cancer-treated-with-stereotactic-body-radiotherapy
#11
Kazuya Takeda, Kentaro Takanami, Yuko Shirata, Takaya Yamamoto, Noriyoshi Takahashi, Kengo Ito, Kei Takase, Keiichi Jingu
We evaluated the reproducibility and predictive value of texture parameters and existing parameters of 18F-FDG PET/CT images in Stage I non-small-cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT). Twenty-six patients with Stage I NSCLC (T1-2N0M0) were retrospectively analyzed. All of the patients underwent an 18F-FDG PET/CT scan before treatment and were treated with SBRT. Each tumor was delineated using PET Edge (MIM Software Inc., Cleveland, OH), and texture parameters were calculated using open-source code CGITA...
September 21, 2017: Journal of Radiation Research
https://www.readbyqxmd.com/read/28989563/generative-method-to-discover-emphysema-subtypes-with-unsupervised-learning-using-lung-macroscopic-patterns-lmps-the-mesa-copd-study
#12
Jingkuan Song, Jie Yang, Benjamin Smith, Pallavi Balte, Eric A Hoffman, R Graham Barr, Andrew F Laine, Elsa D Angelini
Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE) and paraseptal emphysema (PSE). Automated classification methods based on supervised learning are generally based upon the current definition of emphysema subtypes, while unsupervised learning of texture patterns enables the objective discovery of possible new radiological emphysema subtypes. In this work, we use a variant of the Latent Dirichlet Allocation (LDA) model to discover lung macroscopic patterns (LMPs) in an unsupervised way from lung regions that encode emphysematous areas...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28967214/defining-the-end-point-of-mastication-a-conceptual-model
#13
Eli M Gray-Stuart, Jim R Jones, John E Bronlund
The great risks of swallowing are choking and aspiration of food into the lungs. Both are rare in normal functioning humans, which is remarkable given the diversity of foods and the estimated 10 million swallows performed in a lifetime. Nevertheless, it remains a major challenge to define the food properties that are necessary to ensure a safe swallow. Here, the mouth is viewed as a well-controlled processor where mechanical sensory assessment occurs throughout the occlusion-circulation cycle of mastication...
October 2017: Journal of Texture Studies
https://www.readbyqxmd.com/read/28966837/a-methodology-for-texture-feature-based-quality-assessment-in-nucleus-segmentation-of-histopathology-image
#14
Si Wen, Tahsin M Kurc, Yi Gao, Tianhao Zhao, Joel H Saltz, Wei Zhu
CONTEXT: Image segmentation pipelines often are sensitive to algorithm input parameters. Algorithm parameters optimized for a set of images do not necessarily produce good-quality-segmentation results for other images. Even within an image, some regions may not be well segmented due to a number of factors, including multiple pieces of tissue with distinct characteristics, differences in staining of the tissue, normal versus tumor regions, and tumor heterogeneity. Evaluation of quality of segmentation results is an important step in image analysis...
2017: Journal of Pathology Informatics
https://www.readbyqxmd.com/read/28961108/low-dose-lung-ct-image-restoration-using-adaptive-prior-features-from-full-dose-training-database
#15
Yuanke Zhang, Junyan Rong, Hongbing Lu, Yuxiang Xing, Jing Meng
The valuable structure features in full-dose CT (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT image adaptively may result in poor preservation of details/textures in LdCT image. This study aims to explore a novel prior knowledge retrieval and representation paradigm, called adaptive prior features assisted restoration algorithm (APFA), for the purpose of better restoration of the low-dose lung CT images by capturing local features from FdCT scans adaptively...
September 27, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28944403/prediction-of-disease-free-survival-by-the-pet-ct-radiomic-signature-in-non-small-cell-lung-cancer-patients-undergoing-surgery
#16
Margarita Kirienko, Luca Cozzi, Lidija Antunovic, Lisa Lozza, Antonella Fogliata, Emanuele Voulaz, Alexia Rossi, Arturo Chiti, Martina Sollini
PURPOSE: Radiomic features derived from the texture analysis of different imaging modalities e show promise in lesion characterisation, response prediction, and prognostication in lung cancer patients. The present study aimed to identify an images-based radiomic signature capable of predicting disease-free survival (DFS) in non-small cell lung cancer (NSCLC) patients undergoing surgery. METHODS: A cohort of 295 patients was selected. Clinical parameters (age, sex, histological type, tumour grade, and stage) were recorded for all patients...
September 24, 2017: European Journal of Nuclear Medicine and Molecular Imaging
https://www.readbyqxmd.com/read/28943697/localization-of-cardiac-volume-and-patient-features-in-inverse-geometry-x-ray-fluoroscopy
#17
Michael A Speidel, Jordan M Slagowski, David A P Dunkerley, Martin Wagner, Tobias Funk, Amish N Raval
The scanning-beam digital x-ray (SBDX) system is an inverse geometry x-ray fluoroscopy technology that performs real-time tomosynthesis at planes perpendicular to the source-detector axis. The live display is a composite image which portrays sharp features (e.g. coronary arteries) extracted from a 16 cm thick reconstruction volume. We present a method for automatically determining the position of the cardiac volume prior to acquisition of a coronary angiogram. In the algorithm, a single non-contrast frame is reconstructed over a 44 cm thickness using shift-and-add digital tomosynthesis...
February 2017: Proceedings of SPIE
https://www.readbyqxmd.com/read/28939952/2d-and-3d-texture-analysis-to-differentiate-brain-metastases-on-mr-images-proceed-with-caution
#18
Monika Béresová, Andrés Larroza, Estanislao Arana, József Varga, László Balkay, David Moratal
OBJECTIVE: To find structural differences between brain metastases of lung and breast cancer, computing their heterogeneity parameters by means of both 2D and 3D texture analysis (TA). MATERIALS AND METHODS: Patients with 58 brain metastases from breast (26) and lung cancer (32) were examined by MR imaging. Brain lesions were manually delineated by 2D ROIs on the slices of contrast-enhanced T1-weighted (CET1) images, and local binary patterns (LBP) maps were created from each region...
September 22, 2017: Magma
https://www.readbyqxmd.com/read/28934225/harmonizing-the-pixel-size-in-retrospective-computed-tomography-radiomics-studies
#19
Dennis Mackin, Xenia Fave, Lifei Zhang, Jinzhong Yang, A Kyle Jones, Chaan S Ng, Laurence Court
Consistent pixel sizes are of fundamental importance for assessing texture features that relate intensity and spatial information in radiomics studies. To correct for the effects of variable pixel sizes, we combined image resampling with Butterworth filtering in the frequency domain and tested the correction on computed tomography (CT) scans of lung cancer patients reconstructed 5 times with pixel sizes varying from 0.59 to 0.98 mm. One hundred fifty radiomics features were calculated for each preprocessing and field-of-view combination...
2017: PloS One
https://www.readbyqxmd.com/read/28929225/prediction-of-survival-by-texture-based-automated-quantitative-assessment-of-regional-disease-patterns-on-ct-in-idiopathic-pulmonary-fibrosis
#20
Sang Min Lee, Joon Beom Seo, Sang Young Oh, Tae Hoon Kim, Jin Woo Song, Sang Min Lee, Namkug Kim
OBJECTIVES: To retrospectively investigate whether the baseline extent and 1-year change in regional disease patterns on CT can predict survival of patients with idiopathic pulmonary fibrosis (IPF). METHODS: A total of 144 IPF patients with CT scans at the time of diagnosis and 1 year later were included. The extents of five regional disease patterns were quantified using an in-house texture-based automated system. The fibrosis score was defined as the sum of the extent of honeycombing and reticular opacity...
September 19, 2017: European Radiology
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