Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels
暂无分享,去创建一个
Geoffrey G. Zhang | E. Moros | R. Gillies | D. Mackin | L. Court | Y. Balagurunathan | D. Goldgof | M. Schabath | D. Hunt | K. Latifi | Muhammad Shafiq-ul-Hassan | G. Ullah | M. Abdalah
[1] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[2] Mary M. Galloway,et al. Texture analysis using gray level run lengths , 1974 .
[3] Robert King,et al. Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..
[4] James F. Greenleaf,et al. Use of gray value distribution of run lengths for texture analysis , 1990, Pattern Recognit. Lett..
[5] Belur V. Dasarathy,et al. Image characterizations based on joint gray level-run length distributions , 1991, Pattern Recognit. Lett..
[6] Bidyut Baran Chaudhuri,et al. An efficient approach to estimate fractal dimension of textural images , 1992, Pattern Recognit..
[7] Sim Heng Ong,et al. A practical method for estimating fractal dimension , 1995, Pattern Recognit. Lett..
[8] M. Kuo,et al. Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. , 2007, Journal of vascular and interventional radiology : JVIR.
[9] Issam El-Naqa,et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes , 2009, Pattern Recognit..
[10] Huan Yu,et al. Coregistered FDG PET/CT-Based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning , 2009, IEEE Transactions on Medical Imaging.
[11] Bernard Fertil,et al. Texture indexes and gray level size zone matrix. Application to cell nuclei classification , 2009 .
[12] R. Jeraj,et al. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters , 2010, Acta oncologica.
[13] R. Gillies,et al. The biology underlying molecular imaging in oncology: from genome to anatome and back again. , 2010, Clinical radiology.
[14] M. Hatt,et al. Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer , 2011, The Journal of Nuclear Medicine.
[15] Robert J. Gillies,et al. Developing a classifier model for lung tumors in CT-scan images , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.
[16] Olivier Gevaert,et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.
[17] Andre Dekker,et al. Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.
[18] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[19] J. Bradley,et al. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[20] Vicky Goh,et al. Are Pretreatment 18F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy? , 2013, The Journal of Nuclear Medicine.
[21] M. Martel,et al. High quality machine-robust image features: identification in nonsmall cell lung cancer computed tomography images. , 2013, Medical physics.
[22] Frank J. Brooks,et al. On some misconceptions about tumor heterogeneity quantification , 2013, European Journal of Nuclear Medicine and Molecular Imaging.
[23] Balaji Ganeshan,et al. Quantifying tumour heterogeneity with CT , 2013, Cancer imaging : the official publication of the International Cancer Imaging Society.
[24] M. Hatt,et al. Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma , 2013, European Journal of Nuclear Medicine and Molecular Imaging.
[25] Lawrence H. Schwartz,et al. Epidermal growth factor receptor mutation in lung adenocarcinomas: relationship with CT characteristics and histologic subtypes. , 2013, Radiology.
[26] W. Tsai,et al. Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study. , 2014, Translational oncology.
[27] H. Hricak,et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. , 2013, Radiology.
[28] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[29] A OjoJ.,et al. Comparative Analysis of Textural Features Derived from GLCM for Ultrasound Liver Image Classification , 2014 .
[30] Robert J. Gillies,et al. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis , 2015, Scientific Reports.
[31] P. Lambin,et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[32] Geoffrey G. Zhang,et al. Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer , 2015, Translational oncology.
[33] Jinzhong Yang,et al. Measuring Computed Tomography Scanner Variability of Radiomics Features , 2015, Investigative radiology.
[34] Fei Yang,et al. Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards , 2015, Journal of medical imaging.
[35] Peter Balter,et al. Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? , 2015, Medical physics.
[36] Peter A Balter,et al. Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer , 2016 .
[37] W. Tsai,et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging , 2016, Scientific Reports.
[38] Qianjin Feng,et al. Robustness of Radiomic Features in [11C]Choline and [18F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization , 2016, Molecular Imaging and Biology.
[39] H. Aerts,et al. Applications and limitations of radiomics , 2016, Physics in medicine and biology.
[40] M.,et al. Statistical and Structural Approaches to Texture , 2022 .