Classification of parenchymal abnormality in scleroderma lung using a novel approach to denoise images collected via a multicenter study.
暂无分享,去创建一个
Sumit K. Shah | Robert A. Ochs | R. Elashoff | Matthew S. Brown | J. Goldin | F. Abtin | Hyun J. Kim | D. Gjertson | Gang Li | Fah Vasunilashorn | M. Brown | R. Ochs | Matthew S. Brown
[1] David A Lynch,et al. Quantitative CT of fibrotic interstitial lung disease. , 2007, Chest.
[2] Susan A. Murphy,et al. Monographs on statistics and applied probability , 1990 .
[3] E. V. van Beek,et al. Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM). , 2006, Academic radiology.
[4] Charlie Strange,et al. Cyclophosphamide versus placebo in scleroderma lung disease. , 2006, The New England journal of medicine.
[5] Tony F. Chan,et al. Structure-Texture Image Decomposition—Modeling, Algorithms, and Parameter Selection , 2006, International Journal of Computer Vision.
[6] Richard A. Robb,et al. High resolution multidetector CT aided tissue analysis and quantification of lung fibrosis , 2006, SPIE Medical Imaging.
[7] Jin Mo Goo,et al. Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience. , 2005, Radiology.
[8] David A Lynch,et al. Idiopathic interstitial pneumonias: CT features. , 2005, Radiology.
[9] Sumit K. Shah,et al. Computer-aided lung nodule detection in CT: results of large-scale observer test. , 2005, Academic radiology.
[10] Antonin Chambolle,et al. Dual Norms and Image Decomposition Models , 2005, International Journal of Computer Vision.
[11] Ilias Maglogiannis,et al. Characterization of digital medical images utilizing support vector machines , 2004, BMC Medical Informatics Decis. Mak..
[12] M. Nikolova. An Algorithm for Total Variation Minimization and Applications , 2004 .
[13] Mila Nikolova,et al. Regularizing Flows for Constrained Matrix-Valued Images , 2004, Journal of Mathematical Imaging and Vision.
[14] D. Hansell,et al. Obstructive lung diseases: texture classification for differentiation at CT. , 2003, Radiology.
[15] Gary K Grunwald,et al. Quantitative CT indexes in idiopathic pulmonary fibrosis: relationship with physiologic impairment. , 2003, Radiology.
[16] Michael F McNitt-Gray,et al. AAPM/RSNA Physics Tutorial for Residents: Topics in CT. Radiation dose in CT. , 2002, Radiographics : a review publication of the Radiological Society of North America, Inc.
[17] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[18] Patrick Haffner,et al. Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.
[19] E. Hoffman,et al. Computer recognition of regional lung disease patterns. , 1999, American journal of respiratory and critical care medicine.
[20] J. A. Calvin. Regression Models for Categorical and Limited Dependent Variables , 1998 .
[21] S. Mallat. A wavelet tour of signal processing , 1998 .
[22] I. Johnstone,et al. Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .
[23] R. Tibshirani,et al. An Introduction to the Bootstrap , 1995 .
[24] Roel Bosker,et al. Standard Errors and Sample Sizes for Two-Level Research , 1993 .
[25] J. Remy,et al. Computed Tomography Assessment of Ground‐Glass Opacity: Semiology and Significance , 1993, Journal of thoracic imaging.
[26] Milan Sonka,et al. Image Processing, Analysis and Machine Vision , 1993, Springer US.
[27] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[28] W. Webb. High resolution lung computed tomography. Normal anatomic and pathologic findings. , 1991, The Radiologic clinics of North America.
[29] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..