Gray-level invariant Haralick texture features
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Tommy Löfstedt | Patrik Brynolfsson | Tufve Nyholm | Anders Garpebring | Thomas Asklund | Tommy Löfstedt | T. Nyholm | A. Garpebring | T. Asklund | P. Brynolfsson
[1] M. Giger,et al. Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images , 2007, Magnetic resonance in medicine.
[2] Johan Trygg,et al. ADC texture--an imaging biomarker for high-grade glioma? , 2014, Medical physics.
[3] Vasudev Mohan,et al. Kernel-based PSO and FRVM: An automatic plant leaf type detection using texture, shape, and color features , 2016, Comput. Electron. Agric..
[4] 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.
[5] Sang Joon Park,et al. Glioma: Application of Whole-Tumor Texture Analysis of Diffusion-Weighted Imaging for the Evaluation of Tumor Heterogeneity , 2014, PloS one.
[6] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[7] Siegfried Trattnig,et al. Texture‐based classification of focal liver lesions on MRI at 3.0 Tesla: A feasibility study in cysts and hemangiomas , 2010, Journal of magnetic resonance imaging : JMRI.
[8] Welch Bl. THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .
[9] Perry Xiao,et al. In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). , 2014, International journal of pharmaceutics.
[10] H. Hricak,et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores , 2015, European Radiology.
[11] I. T. Jolliffe,et al. Springer series in statistics , 1986 .
[12] F. Ulaby,et al. Textural Infornation in SAR Images , 1986, IEEE Transactions on Geoscience and Remote Sensing.
[13] Paul Scheunders,et al. Wavelets for texture analysis, an overview , 1997 .
[14] Andrzej Materka,et al. Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study. , 2009, Medical physics.
[15] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[16] Ankit Chaudhary,et al. Fabric defect detection based on GLCM and Gabor filter: A comparison , 2013 .
[17] Knut Kvaal,et al. Classification of Dynamic Contrast Enhanced MR Images of Cervical Cancers Using Texture Analysis and Support Vector Machines , 2014, IEEE Transactions on Medical Imaging.
[18] Gabriel Cristóbal,et al. Automated pollen identification using microscopic imaging and texture analysis. , 2015, Micron.
[19] Hyunjin Park,et al. Classification of low-grade and high-grade glioma using multi-modal image radiomics features , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[20] Lothar R. Schad,et al. Problems in texture analysis with magnetic resonance imaging , 2004, Dialogues in clinical neuroscience.
[21] David A Clausi. An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .
[22] Geoffrey G. Zhang,et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels , 2017, Medical physics.
[23] Wagner Coelho A. Pereira,et al. Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound , 2012, IEEE Transactions on Medical Imaging.
[24] Adelin Albert,et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer , 2017, European Journal of Nuclear Medicine and Molecular Imaging.
[25] S. M. Collins,et al. Range- and azimuth-dependent variability of image texture in two-dimensional echocardiograms. , 1983, Circulation.
[26] Hao Chen,et al. Gland segmentation in colon histology images: The glas challenge contest , 2016, Medical Image Anal..
[27] H. Johnson,et al. A comparison of 'traditional' and multimedia information systems development practices , 2003, Inf. Softw. Technol..
[28] Maryellen L. Giger,et al. Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets , 2012, Journal of Digital Imaging.
[29] David A. Clausi,et al. Designing Gabor filters for optimal texture separability , 2000, Pattern Recognit..
[30] M. Stasi,et al. Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness , 2015, Physics in medicine and biology.
[31] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[32] R. N. Macsween,et al. Computer analysis of ultrasonic signals in diffuse liver disease. , 1979, Ultrasound in medicine & biology.
[33] Carlos Fernandez-Lozano,et al. Texture analysis in gel electrophoresis images using an integrative kernel-based approach , 2016, Scientific Reports.
[34] N. Petrick,et al. Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. , 1995, Physics in medicine and biology.
[35] Matti Pietikäinen,et al. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.
[36] Andre Dekker,et al. Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.
[37] Surjya K. Pal,et al. Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images , 2016 .
[38] J. Jonsson,et al. EP-1533: ICE-Studio - An Interactive visual research tool for image analysis , 2015 .
[39] Nasir M. Rajpoot,et al. A Stochastic Polygons Model for Glandular Structures in Colon Histology Images , 2015, IEEE Transactions on Medical Imaging.
[40] Leen-Kiat Soh,et al. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..
[41] El Naqa,et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities , 2015 .
[42] Dawit Assefa,et al. Robust texture features for response monitoring of glioblastoma multiforme on T1-weighted and T2-FLAIR MR images: a preliminary investigation in terms of identification and segmentation. , 2010, Medical physics.
[43] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[44] Chaofeng Liang,et al. A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme , 2017, Scientific Reports.
[45] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[46] Peter Gibbs,et al. Texture analysis in assessment and prediction of chemotherapy response in breast cancer , 2013, Journal of magnetic resonance imaging : JMRI.
[47] B. L. Welch. The generalisation of student's problems when several different population variances are involved. , 1947, Biometrika.
[48] Hon J. Yu,et al. Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. , 2008, Academic radiology.
[49] H. Lyng,et al. Integrative Analysis of DCE-MRI and Gene Expression Profiles in Construction of a Gene Classifier for Assessment of Hypoxia-Related Risk of Chemoradiotherapy Failure in Cervical Cancer , 2016, Clinical Cancer Research.
[50] Johan Trygg,et al. Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters , 2017, Scientific Reports.