Castellano, G., Bonilha, L., Li, L. M., & Cendes, F. (2004). Texture analysis of medical images. Clinical Radiology, 59(12), 1061-1069. doi:10.1016/j.crad.2004.07.008
Hodgdon, T., McInnes, M. D. F., Schieda, N., Flood, T. A., Lamb, L., & Thornhill, R. E. (2015). Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology, 276(3), 787-796. doi:10.1148/radiol.2015142215
Larroza, A., Moratal, D., Paredes-Sánchez, A., Soria-Olivas, E., Chust, M. L., Arribas, L. A., & Arana, E. (2015). Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. Journal of Magnetic Resonance Imaging, 42(5), 1362-1368. doi:10.1002/jmri.24913
[+]
Castellano, G., Bonilha, L., Li, L. M., & Cendes, F. (2004). Texture analysis of medical images. Clinical Radiology, 59(12), 1061-1069. doi:10.1016/j.crad.2004.07.008
Hodgdon, T., McInnes, M. D. F., Schieda, N., Flood, T. A., Lamb, L., & Thornhill, R. E. (2015). Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology, 276(3), 787-796. doi:10.1148/radiol.2015142215
Larroza, A., Moratal, D., Paredes-Sánchez, A., Soria-Olivas, E., Chust, M. L., Arribas, L. A., & Arana, E. (2015). Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. Journal of Magnetic Resonance Imaging, 42(5), 1362-1368. doi:10.1002/jmri.24913
Thevenot, J., Hirvasniemi, J., Pulkkinen, P., Määttä, M., Korpelainen, R., Saarakkala, S., & Jämsä, T. (2014). Assessment of Risk of Femoral Neck Fracture with Radiographic Texture Parameters: A Retrospective Study. Radiology, 272(1), 184-191. doi:10.1148/radiol.14131390
Kassner, A., & Thornhill, R. E. (2010). Texture Analysis: A Review of Neurologic MR Imaging Applications. American Journal of Neuroradiology, 31(5), 809-816. doi:10.3174/ajnr.a2061
Pfeiffer, M. P., & Biederman, R. W. W. (2015). Cardiac MRI. Medical Clinics of North America, 99(4), 849-861. doi:10.1016/j.mcna.2015.02.011
Flett, A. S., Hasleton, J., Cook, C., Hausenloy, D., Quarta, G., Ariti, C., … Moon, J. C. (2011). Evaluation of Techniques for the Quantification of Myocardial Scar of Differing Etiology Using Cardiac Magnetic Resonance. JACC: Cardiovascular Imaging, 4(2), 150-156. doi:10.1016/j.jcmg.2010.11.015
Engan K Eftestøl T Ørn S Kvaloy JT Woie L Exploratory data analysis of image texture and statistical features on myocardium and infarction areas in cardiac magnetic resonance images 2010
Kotu LP Engan K Eftestøl T Ørn S Woie L Segmentation of scarred and non-scarred myocardium in LG enhanced CMR images using intensity-based textural analysis 2011
Kotu, L., Engan, K., Skretting, K., Måløy, F., Ørn, S., Woie, L., & Eftestøl, T. (2013). Probability mapping of scarred myocardium using texture and intensity features in CMR images. BioMedical Engineering OnLine, 12(1), 91. doi:10.1186/1475-925x-12-91
Schofield, R., Ganeshan, B., Kozor, R., Nasis, A., Endozo, R., Groves, A., … Moon, J. C. (2016). CMR myocardial texture analysis tracks different etiologies of left ventricular hypertrophy. Journal of Cardiovascular Magnetic Resonance, 18(S1). doi:10.1186/1532-429x-18-s1-o82
Larroza, A., Materka, A., López-Lereu, M. P., Monmeneu, J. V., Bodí, V., & Moratal, D. (2017). Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging. European Journal of Radiology, 92, 78-83. doi:10.1016/j.ejrad.2017.04.024
Baessler, B., Mannil, M., Oebel, S., Maintz, D., Alkadhi, H., & Manka, R. (2018). Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images. Radiology, 286(1), 103-112. doi:10.1148/radiol.2017170213
Hervas, A., Ruiz-Sauri, A., de Dios, E., Forteza, M. J., Minana, G., Nunez, J., … Bodi, V. (2015). Inhomogeneity of collagen organization within the fibrotic scar after myocardial infarction: results in a swine model and in human samples. Journal of Anatomy, 228(1), 47-58. doi:10.1111/joa.12395
Heiberg, E., Sjögren, J., Ugander, M., Carlsson, M., Engblom, H., & Arheden, H. (2010). Design and validation of Segment - freely available software for cardiovascular image analysis. BMC Medical Imaging, 10(1). doi:10.1186/1471-2342-10-1
Bodí, V., Sanchis, J., López-Lereu, M. P., Losada, A., Núñez, J., Pellicer, M., … Llácer, À. (2005). Usefulness of a Comprehensive Cardiovascular Magnetic Resonance Imaging Assessment for Predicting Recovery of Left Ventricular Wall Motion in the Setting of Myocardial Stunning. Journal of the American College of Cardiology, 46(9), 1747-1752. doi:10.1016/j.jacc.2005.07.039
Rangayyan, R. M., Nguyen, T. M., Ayres, F. J., & Nandi, A. K. (2009). Effect of Pixel Resolution on Texture Features of Breast Masses in Mammograms. Journal of Digital Imaging, 23(5), 547-553. doi:10.1007/s10278-009-9238-0
Materka A Strzelecki M On the importance of MRI nonuniformity correction for texture analysis 2013
Collewet, G., Strzelecki, M., & Mariette, F. (2004). Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magnetic Resonance Imaging, 22(1), 81-91. doi:10.1016/j.mri.2003.09.001
Vallières M MATLAB programming tools for radiomics analysis https://github.com/mvallieres/radiomics
Zhao G Pietikainen M Center for machine vision and signal analysis http://www.cse.oulu.fi/CMV/Downloads/LBPMatlab
Zwanenburg A Leger S Vallières M Löck S Image biomarker standardisation initiative 2017 http://arxiv.org/abs/1612.07003
Vallières, M., Freeman, C. R., Skamene, S. R., & El Naqa, I. (2015). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Physics in Medicine and Biology, 60(14), 5471-5496. doi:10.1088/0031-9155/60/14/5471
Zhao, G., & Pietikainen, M. (2007). Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 915-928. doi:10.1109/tpami.2007.1110
Ojala T Pietikäinen M Mäenpää T A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification
Duan, K.-B., Rajapakse, J. C., Wang, H., & Azuaje, F. (2005). Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data. IEEE Transactions on Nanobioscience, 4(3), 228-234. doi:10.1109/tnb.2005.853657
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Machine Learning, 46(1/3), 389-422. doi:10.1023/a:1012487302797
Wang, S., & Summers, R. M. (2012). Machine learning and radiology. Medical Image Analysis, 16(5), 933-951. doi:10.1016/j.media.2012.02.005
Kuhn, M. (2008). Building Predictive Models inRUsing thecaretPackage. Journal of Statistical Software, 28(5). doi:10.18637/jss.v028.i05
Colby J (multiple) Support Vector Machine Recursive Feature Elimination - mSVM-RFE http://www.colbyimaging.com/wiki/statistics/msvm-rfe
Salzberg, S. L. (1997). Data Mining and Knowledge Discovery, 1(3), 317-328. doi:10.1023/a:1009752403260
Bodí, V., Husser, O., Sanchis, J., Núñez, J., López-Lereu, M. P., Monmeneu, J. V., … Llácer, A. (2010). Contractile Reserve and Extent of Transmural Necrosis in the Setting of Myocardial Stunning: Comparison at Cardiac MR Imaging. Radiology, 255(3), 755-763. doi:10.1148/radiol.10091191
Bodi, V., Monmeneu, J. V., Ortiz-Perez, J. T., Lopez-Lereu, M. P., Bonanad, C., Husser, O., … Chorro, F. J. (2016). Prediction of Reverse Remodeling at Cardiac MR Imaging Soon after First ST-Segment–Elevation Myocardial Infarction: Results of a Large Prospective Registry. Radiology, 278(1), 54-63. doi:10.1148/radiol.2015142674
Shriki, J. E., Surti, K. S., Farvid, A. F., Lee, C. C., Samadi, S., Hirschbeinv, J., & Colletti, P. M. (2011). Chemical Shift Artifact on Steady-State Free Precession Cardiac Magnetic Resonance Sequences as a Result of Lipomatous Metaplasia: A Novel Finding in Chronic Myocardial Infarctions. Canadian Journal of Cardiology, 27(5), 664.e17-664.e23. doi:10.1016/j.cjca.2010.12.074
Goldfarb, J. W., McLaughlin, J., Gray, C. A., & Han, J. (2011). Cyclic CINE-balanced steady-state free precession image intensity variations: Implications for the detection of myocardial edema. Journal of Magnetic Resonance Imaging, 33(3), 573-581. doi:10.1002/jmri.22368
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: Images Are More than Pictures, They Are Data. Radiology, 278(2), 563-577. doi:10.1148/radiol.2015151169
[-]