Radiomics: a new application from established techniques

References

• Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27(3):379–423.
   Google Scholar
• Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Transactions Systems, Man Cybernetics. 1973;SMC-3(6):610–621.
   Web of Science ®Google Scholar
• Galloway MM. Texture analysis using gray level run lengths. Comput Graphics Image Process. 1975;4(2):172–179.
   Google Scholar
• Amadasun M, King R. Textural features corresponding to textural properties. IEEE Transactions Systems, Man Cybernetics. 1989;19(5):1264–1274.
   Web of Science ®Google Scholar
• Kaizer H. A quantification of textures on aerial photographs. Tech Note 121. Boston (MA): Boston University Research Lab; 1955.
   Google Scholar
• Sutton RN, Hall EL. Texture measures for automatic classification of pulmonary disease. IEEE Transactions on Computers. 1972;21(7):667–676.
   Web of Science ®Google Scholar
• Bardeen JM, Carter B, Hawking SW. The four laws of black hole mechanics. Commun Math Phys. 1973;31(2):161–170.
   Web of Science ®Google Scholar
• Bekenstein JD. Black holes and entropy. Phys Rev D. 1973;7(8):2333–2346.
   Web of Science ®Google Scholar
• Sonntag R, Borgnakke C, Van Wylen G. Fundamentals of thermodynamics. Ann Arbor (MI): The University of Michigan, Wiley; 1998.
   Google Scholar
• Eisert J, Cramer M, Plenio MB. Area laws for the entanglement entropy. Rev Mod Phys. 2010;82(1):277–306.
   Web of Science ®Google Scholar
• Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.
   PubMed Web of Science ®Google Scholar
• Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–446.
   PubMed Web of Science ®Google Scholar
• Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–1248.
   PubMed Web of Science ®Google Scholar
• Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2015;278:151169.
   Google Scholar
• Larkin TJ, Canuto HC, Kettunen MI, et al. Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment. Magn Reson Med. 2014;71(1):402–410.
   PubMed Web of Science ®Google Scholar
• Tixier F, Le Rest CC, Hatt M, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med. 2011;52(3):369–378.
   PubMed Web of Science ®Google Scholar
• Knuth KH. Optimal data-based binning for histograms. Arxiv Preprint Physics/0605197. 2006.
   Google Scholar
• Shimazaki H, Shinomoto S. A method for selecting the bin size of a time histogram. Neural Comput. 2007;19(6):1503–1527.
   PubMed Web of Science ®Google Scholar
• Raeth U, Schlaps D, Limberg B, et al. Diagnostic accuracy of computerized B‐scan texture analysis and conventional ultrasonography in diffuse parenchymal and malignant liver disease. J Clin Ultrasound. 1985;13(2):87–99.
   PubMed Web of Science ®Google Scholar
• Garra BS, Krasner BH, Horii SC, et al. Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis. Ultrason Imaging. 1993;15(4):267–285.
   PubMed Web of Science ®Google Scholar
• Sujana H, Swarnamani S, Suresh S. Application of artificial neural networks for the classification of liver lesions by image texture parameters. Ultrasound Med Biol. 1996;22(9):1177–1181.
   PubMed Web of Science ®Google Scholar
• Brinkmann G, Melchert UH, Lalk G, et al. The total entropy for evaluating 31P-magnetic resonance spectra of the liver in healthy volunteers and patients with metastases. Invest Radiol. 1997;32(2):100–104.
   PubMed Web of Science ®Google Scholar
• Mayr NA, Yuh WT, Arnholt JC, et al. Pixel analysis of MR perfusion imaging in predicting radiation therapy outcome in cervical cancer. J Magn Reson Imaging. 2000;12(6):1027–1033.
   PubMed Web of Science ®Google Scholar
• Bernasconi A, Antel SB, Collins DL, et al. Texture analysis and morphological processing of magnetic resonance imaging assist detection of focal cortical dysplasia in extra‐temporal partial epilepsy. Ann Neurol. 2001;49(6):770–775.
   PubMed Web of Science ®Google Scholar
• Hayes C, Padhani AR, Leach MO. Assessing changes in tumour vascular function using dynamic contrast-enhanced magnetic resonance imaging. NMR Biomed. 2002;15(2):154–163.
   PubMed Web of Science ®Google Scholar
• Jirak D, Dezortová M, Taimr P, et al. Texture analysis of human liver. J Magn Reson Imaging. 2002;15(1):68–74.
   PubMed Web of Science ®Google Scholar
• Bernasconi A. Advanced MRI analysis methods for detection of focal cortical dysplasia. Epileptic Disorders: International Epilepsy Journal with Videotape. 2003;5(Suppl 2):S81–84.
   PubMedGoogle Scholar
• Bonilha L, Kobayashi E, Castellano G, et al. Texture analysis of hippocampal sclerosis. Epilepsia. 2003;44(12):1546–1550.
   PubMed Web of Science ®Google Scholar
• Yoshida H, Casalino DD, Keserci B, et al. Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images. Phys Med Biol. 2003;48(22):3735–3753.
   PubMed Web of Science ®Google Scholar
• Chabat F, Yang G-Z, Hansell DM. Obstructive lung diseases: texture classification for differentiation at CT 1. Radiology. 2003;228(3):871–877.
   PubMed Web of Science ®Google Scholar
• Herlidou-Même S, Constans J, Carsin B, et al. MRI texture analysis on texture test objects, normal brain and intracranial tumors. Magn Reson Imaging. 2003;21(9):989–993.
   PubMed Web of Science ®Google Scholar
• Chang Y-C, Huang C-S, Liu Y-J, et al.Angiogenic response of locally advanced breast cancer to neoadjuvant chemotherapy evaluated with parametric histogram from dynamic contrast-enhanced MRI. Phys Med Biol. 2004;49(16):3593–3602.
   PubMed Web of Science ®Google Scholar
• de Lussanet QG, Backes WH, Griffioen AW, et al. Dynamic contrast-enhanced magnetic resonance imaging of radiation therapy-induced microcirculation changes in rectal cancer. Int J Radiat Oncol Biol Phys. 2005;63(5):1309–1315.
   PubMed Web of Science ®Google Scholar
• Xu Y, Sonka M, McLennan G, et al. MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies. IEEE Trans Med Imaging. 2006;25(4):464–475.
   PubMed Web of Science ®Google Scholar
• Ertaş G, Gülçür HO, Tunaci M. Improved lesion detection in MR mammography: three-dimensional segmentation, moving voxel sampling, and normalized maximum intensity-time ratio entropy. Acad Radiol. 2007;14(2):151–161.
   PubMed Web of Science ®Google Scholar
• Jackson A, O’Connor JPB, Parker GJM, et al. Imaging tumor vascular heterogeneity and angiogenesis using dynamic contrast-enhanced magnetic resonance imaging. Clin Cancer Res. 2007;13(12):3449–3459.
   PubMed Web of Science ®Google Scholar
• Caban JJ, Yao J, Avila NA, et al. Texture-based computer-aided diagnosis system for lung fibrosis. Proc SPIE 6514, Medical Imaging: Computer-Aided Diagnosis. 2007;651439. doi:10.1117/12.709831
   Google Scholar
• Depeursinge A, Sage D, Hidki A, et al. Lung tissue classification using wavelet frames. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2007:6259–6262.
   Google Scholar
• Karahaliou A, Skiadopoulos S, Boniatis I, et al. Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis. Br J Radiol. 2007;80(956):648–656.
   PubMed Web of Science ®Google Scholar
• Kontos D, Bakic PR, Carton A-K, et al. Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study. Acad Radiol. 2009;16(3):283–298.
   PubMed Web of Science ®Google Scholar
• Mayerhoefer ME, Schima W, Trattnig S, et al. Texture‐based classification of focal liver lesions on MRI at 3.0 Tesla: A feasibility study in cysts and hemangiomas. J Magn Reson Imaging. 2010;32(2):352–359.
   PubMed Web of Science ®Google Scholar
• Holli K, Lääperi A-L, Harrison L, et al. Characterization of breast cancer types by texture analysis of magnetic resonance images. Acad Radiol. 2010;17(2):135–141.
   PubMed Web of Science ®Google Scholar
• Chuah TK, Poh CL, Sheah K Quantitative texture analysis of MRI images for detection of cartilage-related bone marrow edema. Conference proceedings: … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference. 2011;2011:5112–5115.
   Google Scholar
• Cui J-L, Wen C-Y, Hu Y, et al. Entropy-based analysis for diffusion anisotropy mapping of healthy and myelopathic spinal cord. Neuroimage. 2011;54(3):2125–2131.
   PubMed Web of Science ®Google Scholar
• O’Connor J, Rose C, Jackson A, et al. DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6. Br J Cancer. 2011;105(1):139–145.
   PubMed Web of Science ®Google Scholar
• Fujimoto K, Tonan T, Azuma S, et al. Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C: correlation with pathologic fibrosis stage and inflammatory activity grade. Radiology. 2011;258(3):739–748.
   PubMed Web of Science ®Google Scholar
• Vaidya M, Creach KM, Frye J, et al. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol. 2012;102(2):239–245.
   PubMed Web of Science ®Google Scholar
• Chen Y, Pham TD. Sample entropy and regularity dimension in complexity analysis of cortical surface structure in early Alzheimer’s disease and aging. J Neurosci Methods. 2013;215(2):210–217.
   PubMed Web of Science ®Google Scholar
• Chicklore S, Goh V, Siddique M, et al. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40(1):133–140.
   PubMed Web of Science ®Google Scholar
• Leijenaar RTH, Carvalho S, Velazquez ER, et al. Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability. Acta Oncol (Madr). 2013;52(7):1391–1397.
   PubMed Web of Science ®Google Scholar
• Kierans AS, Bennett GL, Mussi TC, et al.Characterization of malignancy of adnexal lesions using ADC entropy: comparison with mean ADC and qualitative DWI assessment. J Magn Reson Imaging. 2013;37(1):164–171.
   PubMed Web of Science ®Google Scholar
• Foroutan P, Kreahling JM, Morse DL, et al. Diffusion MRI and novel texture analysis in osteosarcoma xenotransplants predicts response to anti-checkpoint therapy. PLoS One. 2013;8(12):e82875.
   PubMedGoogle Scholar
• Sato JR, Takahashi DY, Hoexter MQ, et al. Measuring network’s entropy in ADHD: a new approach to investigate neuropsychiatric disorders. Neuroimage. 2013;77:44–51.
   PubMed Web of Science ®Google Scholar
• Suoranta S, Holli-Helenius K, Koskenkorva P, et al. 3D texture analysis reveals imperceptible MRI textural alterations in the thalamus and putamen in progressive myoclonic epilepsy type 1, EPM1. PLoS One. 2013;8(7):e69905.
   PubMedGoogle Scholar
• Cao M-Q, Suo S-T, Zhang X-B, et al. Entropy of T2-weighted imaging combined with apparent diffusion coefficient in prediction of uterine leiomyoma volume response after uterine artery embolization. Acad Radiol. 2014;21(4):437–444.
   PubMed Web of Science ®Google Scholar
• Ryu YJ, Choi SH, Park SJ, et al. Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity. PLoS One. 2014;9(9):e108335.
   PubMedGoogle Scholar
• Suo S-T, Chen XX, Fan Y, et al. Histogram analysis of apparent diffusion coefficient at 3.0 T in urinary bladder lesions: correlation with pathologic findings. Acad Radiol. 2014;21(8):1027–1034.
   PubMed Web of Science ®Google Scholar
• Parmar C, Velazquez ER, Leijenaar R, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. Plos One. 2014;9(7):e102107.
   PubMedGoogle Scholar
• Coroller TP, Grossmann P, Hou Y, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 2015;114(3):345–350.
   PubMed Web of Science ®Google Scholar
• Leijenaar RT, Carvalho S, Hoebers FJ, et al. External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol (Madr). 2015;54:1–7.
   Google Scholar
• Panth KM, Leijenaar RTH, Carvalho S, et al. Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells. Radiother Oncol. 2015;116(3):462–466.
   PubMed Web of Science ®Google Scholar
• Parmar C, Grossmann P, Bussink J, et al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep. 2015;5:13087.
   PubMed Web of Science ®Google Scholar
• Parmar C, Leijenaar RTH, Grossmann P, et al. Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep. 2015;5:11044.
   PubMed Web of Science ®Google Scholar
• Grove O, Berglund AE, Schabath MB, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. Plos One. 2015;10(3):e0118261.
   Google Scholar
• Sinha S, Lucas‐Quesada FA, Debruhl ND, et al. Multifeature analysis of Gd‐enhanced MR images of breast lesions. J Magn Reson Imaging. 1997;7(6):1016–1026.
   PubMed Web of Science ®Google Scholar
• Cui J-L, Wen C-Y, Hu Y, et al. Entropy-based analysis for diffusion anisotropy mapping of healthy and myelopathic spinal cord. Neuroimage. 2011;54(3):2125–2131.
   PubMed Web of Science ®Google Scholar
• Chen W, Giger ML, Li H, et al. Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images. Magn Reson Med. 2007;58(3):562–571.
   PubMed Web of Science ®Google Scholar
• Ahmed A, Gibbs P, Pickles M, et al. Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging. 2013;38(1):89–101.
   PubMed Web of Science ®Google Scholar
• Magnin IE, Cluzeau F, Odet CL, et al. Mammographic texture analysis: an evaluation of risk for developing breast cancer. Opt Eng. 1986;25(6):156780–156780.
   Google Scholar
• Wu C-M, Chen Y-C, Hsieh K-S. Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging. 1992;11(2):141–152.
   PubMed Web of Science ®Google Scholar
• Mir A, Hanmandlu M, Tandon S. Texture analysis of CT images. IEEE Eng Med Biol Magazine. 1995;14(6):781–786.
   Google Scholar
• Wei D, Chan HP, Helvie MA, et al. Classification of mass and normal breast tissue on digital mammograms: multiresolution texture analysis. Med Phys. 1995;22(9):1501–1513.
   PubMed Web of Science ®Google Scholar
• Sun Y, Horng M-H, Lin X, et al. Ultrasonic image analysis for liver diagnosis. IEEE Eng Med Biol Magazine. 1996;15(6):93–101.
   Google Scholar
• Lucht R, Brix G, Lorenz W. Texture analysis of differently reconstructed PET images. Phys Med Biol. 1996;41(10):2207–2219.
   PubMed Web of Science ®Google Scholar
• Petrick N, Chan HP, Wei D, et al. Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification. Med Phys. 1996;23(10):1685–1696.
   PubMed Web of Science ®Google Scholar
• Sahiner B, Chan H-P, Petrick N, et al. Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging. 1996;15(5):598–610.
   PubMed Web of Science ®Google Scholar
• Chan HP, Sahiner B, Petrick N, et al. Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network. Phys Med Biol. 1997;42(3):549–567.
   PubMed Web of Science ®Google Scholar
• Wei D, Chan HP, Petrick N, et al. False-positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis. Med Phys. 1997;24(6):903–914.
   PubMed Web of Science ®Google Scholar
• Freeborough P, Fox NC. MR image texture analysis applied to the diagnosis and tracking of Alzheimer’s disease. IEEE Trans Med Imaging. 1998;17(3):475–478.
   PubMed Web of Science ®Google Scholar
• Sahiner B, Chan HP, Petrick N, et al. Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis. Med Phys. 1998;25(4):516–526.
   PubMed Web of Science ®Google Scholar
• Chen E, Chung P-C, Chen C-L, et al. An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Engineering. 1998;45(6):783–794.
   PubMed Web of Science ®Google Scholar
• Mudigonda NR, Rangayyan RM, Desautels JE. Gradient and texture analysis for the classification of mammographic masses. IEEE Trans Med Imaging. 2000;19(10):1032–1043.
   PubMed Web of Science ®Google Scholar
• Kovalev V, Kruggel F, Gertz HJ, et al. Three-dimensional texture analysis of MRI brain datasets. IEEE Trans Med Imaging. 2001;20(5):424–433.
   PubMed Web of Science ®Google Scholar
• Sivaramakrishna R, Powell KA, Lieber ML, et al. Texture analysis of lesions in breast ultrasound images. Comput Med Imaging Graph. 2002;26(5):303–307.
   PubMed Web of Science ®Google Scholar
• Horng M-H, Sun Y-N, Lin X-Z. Texture feature coding method for classification of liver sonography. Comput Med Imag Grap. 2002;26(1):33–42.
   PubMed Web of Science ®Google Scholar
• Gibbs P, Turnbull LW. Textural analysis of contrast‐enhanced MR images of the breast. Magn Reson Med. 2003;50(1):92–98.
   PubMed Web of Science ®Google Scholar
• Gletsos M, Mougiakakou SG, Matsopoulos GK, et al. A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE Trans Inf Technol Biomed. 2003;7(3):153–162.
   PubMedGoogle Scholar
• Mahmoud-Ghoneim D, Toussaint G, Constans JM, et al. Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging. 2003;21(9):983–987.
   PubMed Web of Science ®Google Scholar
• Pereira RR Jr, Marques PMA, Honda MO, et al. Usefulness of texture analysis for computerized classification of breast lesions on mammograms. J Digit Imaging. 2007;20(3):248–255.
   PubMed Web of Science ®Google Scholar
• Nie K, Chen J-H, Hon JY, et al. Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol. 2008;15(12):1513–1525.
   PubMed Web of Science ®Google Scholar
• El Naqa I, Grigsby P, Apte A, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009;42(6):1162–1171.
   PubMed Web of Science ®Google Scholar
• Kassner A, Liu F, Thornhill RE, et al. Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1‐weighted MR images. J Magn Reson Imaging. 2009;30(5):933–941.
   PubMed Web of Science ®Google Scholar
• McLaren CE, Chen W-P, Nie K, et al. Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. Acad Radiol. 2009;16(7):842–851.
   PubMed Web of Science ®Google Scholar
• Mayerhoefer ME, Welsch GH, Riegler G, et al. Feasibility of texture analysis for the assessment of biochemical changes in meniscal tissue on T1 maps calculated from delayed gadolinium-enhanced magnetic resonance imaging of cartilage data: comparison with conventional relaxation time measurements. Invest Radiol. 2010;45(9):543–547.
   PubMed Web of Science ®Google Scholar
• Karahaliou A, Vassiou K, Arikidis NS, et al. Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis. Br J Radiol. 2010;83(988):296–309.
   PubMed Web of Science ®Google Scholar
• Korfiatis PD, Karahaliou AN, Kazantzi AD, et al. Texture-based identification and characterization of interstitial pneumonia patterns in lung multidetector CT. IEEE Trans Inf Technol Biomed. 2010;14(3):675–680.
   PubMedGoogle Scholar
• Mayerhoefer ME, Stelzeneder D, Bachbauer W, et al. Quantitative analysis of lumbar intervertebral disc abnormalities at 3.0 Tesla: value of T(2) texture features and geometric parameters. NMR Biomed. 2012;25(6):866–872.
   PubMed Web of Science ®Google Scholar
• Gatenby RA, Grove O, Gillies RJ. Quantitative imaging in cancer evolution and ecology. Radiology. 2013;269(1):8–14.
   PubMed Web of Science ®Google Scholar
• Cai H, Liu L, Peng Y, et al. Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols. BMC Cancer. 2014;14:366.
   PubMed Web of Science ®Google Scholar
• Cai H, Peng Y, Ou C, et al. Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR: a machine learning approach. PLoS One. 2014;9(1):e87387.
   PubMed Web of Science ®Google Scholar
• Stember JN, Deng F-M, Taneja SS, et al. Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features. J Magn Reson Imaging. 2014;40(2):301–305.
   PubMed Web of Science ®Google Scholar
• Wang T-C, Huang Y-H, Huang C-S, et al. Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis. Magn Reson Imaging. 2014;32(3):197–205.
   PubMed Web of Science ®Google Scholar
• Chu A, Sehgal CM, Greenleaf JF. Use of gray value distribution of run lengths for texture analysis. Pattern Recognit Lett. 1990;11(6):415–419.
   Web of Science ®Google Scholar
• Dasarathy BV, Holder EB. Image characterizations based on joint gray level—run length distributions. Pattern Recognit Lett. 1991;12(8):497–502.
   Web of Science ®Google Scholar
• Cook GJR, Yip C, Siddique M, et al. Are pretreatment 18F-FDG PET tumor textural features in non–small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54(1):19–26.
   PubMed Web of Science ®Google Scholar
• Mandelbrot BB. How long is the coast of Britain. Science. 1967;156(3775):636–638.
   PubMed Web of Science ®Google Scholar
• Mandelbrot BB. The fractal geometry of nature. Vol. 173. Macmillan; 1983.
   Google Scholar
• Lopes R, Betrouni N. Fractal and multifractal analysis: a review. Med Image Anal. 2009;13(4):634–649.
   PubMed Web of Science ®Google Scholar
• Li H, Giger ML, Olopade OI, et al. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol. 2007;14(5):513–521.
   PubMed Web of Science ®Google Scholar
• Guo Q, Shao J, Ruiz VF. Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms. Int J Comput Assist Radiol Surg. 2009;4(1):11–25.
   PubMed Web of Science ®Google Scholar
• Gagnepain J, Roques-Carmes C. Fractal approach to two-dimensional and three-dimensional surface roughness. Wear. 1986;109(1–4):119–126.
   Web of Science ®Google Scholar
• Sarkar N, Chaudhuri B. An efficient differential box-counting approach to compute fractal dimension of image. IEEE Transactions Systems, Man Cybernetics. 1994;24(1):115–120.
   Web of Science ®Google Scholar
• Peleg S, Naor J, Hartley R, et al. Multiple resolution texture analysis and classification. IEEE Trans Pattern Anal Mach Intell. 1984;PAMI-6(4):518–523.
   PubMed Web of Science ®Google Scholar
• Pentland AP. Fractal-based description of natural scenes. IEEE Trans Pattern Anal Mach Intell. 1984;PAMI-6(6):661–674.
   PubMed Web of Science ®Google Scholar
• Mandelbrot BB, Van Ness JW. Fractional Brownian motions, fractional noises and applications. SIAM Review. 1968;10(4):422–437.
   Web of Science ®Google Scholar
• Rangayyan RM, Mudigonda NR, Desautels JE. Boundary modelling and shape analysis methods for classification of mammographic masses. Med Biol Eng Comput. 2000;38(5):487–496.
   PubMed Web of Science ®Google Scholar
• Rose CJ, Mills SJ, O’Connor JP, et al. Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps. Magn Reson Med. 2009;62(2):488–499.
   PubMed Web of Science ®Google Scholar
• Randen T, Husoy JH. Filtering for texture classification: A comparative study. IEEE Trans Pattern Anal Mach Intell. 1999;21(4):291–310.
   Web of Science ®Google Scholar
• Ganeshan B, Miles KA, Young R, et al. Hepatic entropy and uniformity: additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT. Clin Radiol. 2007;62(8):761–768.
   PubMed Web of Science ®Google Scholar
• Ganeshan B, Miles KA, Young RC, et al. In search of biologic correlates for liver texture on portal-phase CT. Acad Radiol. 2007;14(9):1058–1068.
   PubMed Web of Science ®Google Scholar
• Miles KA, Ganeshan B, Griffiths MR, et al. Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival 1. Radiology. 2009;250(2):444–452.
   PubMed Web of Science ®Google Scholar
• Ganeshan B, Abaleke S, Young RC, et al. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging. 2010;10(1):137–143.
   PubMed Web of Science ®Google Scholar
• Ganeshan B, Burnand K, Young R, et al. Dynamic contrast-enhanced texture analysis of the liver: initial assessment in colorectal cancer. Invest Radiol. 2011;46(3):160–168.
   PubMed Web of Science ®Google Scholar
• Wachinger C, Entropy NN. Laplacian images: structural representations for multi-modal registration. Med Image Anal. 2012;16(1):1–17.
   PubMed Web of Science ®Google Scholar
• Ganeshan B, Panayiotou E, Burnand K, et al. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol. 2012;22(4):796–802.
   PubMed Web of Science ®Google Scholar
• Ganeshan B, Goh V, Mandeville HC, et al. Non–small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology. 2013;266(1):326–336.
   PubMed Web of Science ®Google Scholar
• Ng F, Ganeshan B, Kozarski R, et al. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology. 2013;266(1):177–184.
   PubMed Web of Science ®Google Scholar
• Laws KI. Rapid texture identification. Proc SPIE 0238, Image Processing Missile Guidance. 1980;376–381. doi:10.1117/12.959169
   Google Scholar
• Laws KI. Textured image segmentation [Ph.D. Dissertation]. Los Angeles, California, Image Processing Institute, University of Southern California; 1980.
   Google Scholar
• Cox G, Hoare F, de Jager G. Experiments in lung cancer nodule detection using texture analysis and neural network classifiers. Third South African Workshop Pattern Recognition. 1992;31:136–142.
   Google Scholar
• Miller P, Astley S. Classification of breast tissue by texture analysis. Image Vis Comput. 1992;10(5):277–282.
   Web of Science ®Google Scholar
• Chu Y, Li L, Goldgof DB, et al. Classification of masses on mammograms using support vector machine. Proc SPIE 5032, Medical Imaging: Image Processing. 2003;5032:940–948.
   Google Scholar
• Poonguzhali S, Ravindran G. Automatic classification of focal lesions in ultrasound liver images using combined texture features. Inf Technol J. 2008;7(1):205–209.
   Google Scholar
• Awad J, Krasinski A, Parraga G, et al. Texture analysis of carotid artery atherosclerosis from three-dimensional ultrasound images. Med Phys. 2010;37(4):1382–1391.
   PubMed Web of Science ®Google Scholar
• Dheeba J, Tamil Selvi S. Classification of malignant and benign microcalcification using SVM classifier. Int Conf Emerging Trends Electrical Comput Technol (ICETECT). 2011;7950:686–690.
   Google Scholar
• Barata C, Marques JS, Mendonça T. Bag-of-features classification model for the diagnose of melanoma in dermoscopy images using color and texture descriptors. Image Analysis and Recognition. 2013;7950:547–555.
   Google Scholar
• Virmani J, Kumar V, Kalra N, et al. PCA-SVM based CAD System for Focal liver lesions using B-mode ultrasound images. Def Sci J. 2013;63(5):478–486.
   Web of Science ®Google Scholar
• Pereyra LC, Rangayyan RM, Ponciano-Silva M, et al. Fractal analysis for computer-aided diagnosis of diffuse pulmonary diseases in HRCT images. IEEE Int Symp Med Measurements Appl (MeMeA). 2014;1–6.
   Google Scholar
• Dilger S, Judisch A, Uthoff J, et al. Improved pulmonary nodule classification utilizing lung parenchyma texture features. Proc SPIE 9414, Medical Imaging: Computer-Aided Diagnosis. 2015;9414:94142T.
   Google Scholar
• Mitrea D, Nedevschi S, Abrudean M. Classification of the liver tumors using co-occurrence matrices of textural microstructures. J Commun Comput. 2015;12:6–12.
   Google Scholar
• Singh BK, Verma K, Thoke A. Adaptive gradient descent backpropagation for classification of breast tumors in ultrasound imaging. Procedia Comput Sci. 2015;46:1601–1609.
   Google Scholar
• Al-Kadi OS, Watson D. Texture analysis of aggressive and nonaggressive lung tumor CE CT images. IEEE Trans Biomed Eng. 2008;55(7):1822–1830.
   PubMed Web of Science ®Google Scholar
• Agner SC, Soman S, Libfeld E, et al. Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging. 2011;24(3):446–463.
   PubMed Web of Science ®Google Scholar
• Haar A. Zur theorie der orthogonalen funktionensysteme. Mathematische Annalen. 1910;69(3):331–371.
   Google Scholar
• Daubechies I. Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math. 1988;41(7):909–996.
   Web of Science ®Google Scholar
• Daubechies I. Ten lectures on wavelets. Vol. 61. Philadelphia (PA): Society for Industrial and Applied Mathematics; 1992.
   Google Scholar
• Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell. 1989;11(7):674–693.
   Web of Science ®Google Scholar
• Chen D-R, Chang R-F, Kuo W-J, et al. Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound Med Biol. 2002;28(10):1301–1310.
   PubMed Web of Science ®Google Scholar
• Akhbardeh A, Jacobs MA. Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation. Med Phys. 2012;39(4):2275–2289.
   PubMed Web of Science ®Google Scholar
• Duda RO, Hart PE. Pattern classification and scene analysis. Vol. 3. New York: Wiley; 1973.
   Google Scholar
• Amaldi E, Kann V. On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor Comput Sci. 1998;209(1–2):237–260.
   Web of Science ®Google Scholar
• Pearson K LIII. On lines and planes of closest fit to systems of points in space. London, Edinburgh, Dublin Philos Mag J Sci. 1901;2(11):559–572.
   Google Scholar
• Torgerson WS. Multidimensional scaling: I. Theory and method. Psychometrika. 1952;17(4):401–419.
   Google Scholar
• Coifman RR, Lafon S, Lee AB, et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps. Proc Natl Acad Sci U S A. 2005;102(21):7426–7431.
   PubMed Web of Science ®Google Scholar
• Tenenbaum JB, De Silva V, Langford JC. A global geometric framework for nonlinear dimensionality reduction. Science. 2000;290(5500):2319–2323.
   PubMed Web of Science ®Google Scholar
• Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding. Science. 2000;290(5500):2323–2326.
   PubMed Web of Science ®Google Scholar
• Van Der Maaten L, Postma E. Van den Herik J. Dimensionality reduction: a comparative. J Mach Learn Res. 2009;10:66–71.
   Google Scholar
• Keller JM, Gray MR, Givens JA. A fuzzy k-nearest neighbor algorithm. IEEE Transactions Systems, Man Cybernetics. 1985;SMC-15(4):580–585.
   Web of Science ®Google Scholar
• Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1):21–27.
   Web of Science ®Google Scholar
• Breiman L. Bagging predictors. Mach Learn. 1996;24(2):123–140.
   Web of Science ®Google Scholar
• Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297.
   Web of Science ®Google Scholar
• MacQueen J. Some methods for classification and analysis of multivariate observations. Proc Fifth Berkeley Symp Math Stat Probab. 1967;1(14):281–297.
   Google Scholar
• Fred AL, Jain AK. Data clustering using evidence accumulation. 16th Int Confpattern Recognit, 2002. 2002;4:276–280.
   Google Scholar
• McQuitty LL. Hierarchical linkage analysis for the isolation of types. Educ Psychol Meas. 1960;20(1):55–67.
   Web of Science ®Google Scholar
• Kassner A, Thornhill R. Texture analysis: a review of neurologic MR imaging applications. Am J Neuroradiology. 2010;31(5):809–816.
   PubMed Web of Science ®Google Scholar
• Miles KA, Ganeshan B, Hayball MP. CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging. 2013;13(3):400–406.
   PubMed Web of Science ®Google Scholar
• de Melo RH, Vieira EdA CA. Characterizing the lacunarity of objects and image sets and its use as a technique for the analysis of textural patterns. Adv Concepts for Intell Vision Syst. 2006;4179:208–219.
   Google Scholar
• Nandi R, Nandi AK, Rangayyan RM, et al. Classification of breast masses in mammograms using genetic programming and feature selection. Med Biol Eng Comput. 2006;44(8):683–694.
   PubMed Web of Science ®Google Scholar
• Tourassi GD, Delong DM, Floyd CE Jr. A study on the computerized fractal analysis of architectural distortion in screening mammograms. Phys Med Biol. 2006;51(5):1299–1312.
   PubMed Web of Science ®Google Scholar
• Rangayyan RM, Nguyen TM. Fractal analysis of contours of breast masses in mammograms. J Digit Imaging. 2007;20(3):223–237.
   PubMed Web of Science ®Google Scholar
• Chen D-R, Chang R-F, Huang Y-L, et al. Texture analysis of breast tumors on sonograms. Seminars Ultrasound, CT MRI. 2000;21(4):308–316.
   PubMed Web of Science ®Google Scholar
• Parikh J, Selmi M, Charles-Edwards G, et al. Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy. Radiology. 2014;272(1):100–112.
   PubMed Web of Science ®Google Scholar
• Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–892.
   PubMed Web of Science ®Google Scholar
• Huang Y-L, Chen J-H, Shen W-C. Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images. Acad Radiol. 2006;13(6):713–720.
   PubMed Web of Science ®Google Scholar
• Poonguzhali S, Ravindran G. Performance evaluation of feature extraction methods for classifying abnormalities in ultrasound liver images using neural network. IEEE 28th Annu Int Conf Eng Med Biol Soc. 2006: 2006;4791–4794.
   Google Scholar
• Mittal D, Kumar V, Saxena SC, et al. Neural network based focal liver lesion diagnosis using ultrasound images. Comput Med Imaging Graphics. 2011;35(4):315–323.
   PubMed Web of Science ®Google Scholar
• Virmani J, Kumar V, Kalra N, et al. A rapid approach for prediction of liver cirrhosis based on first order statistics. 2011 Int Conf Multimedia, Process Commun Technologies (IMPACT). 2011;212–215.
   Google Scholar
• Jeon JH, Choi JY, Lee S, et al. Multiple ROI selection based focal liver lesion classification in ultrasound images. Expert Syst Appl. 2013;40(2):450–457.
   Web of Science ®Google Scholar
• Virmani J, Kumar V, Kalra N, et al. SVM-based characterisation of liver cirrhosis by singular value decomposition of GLCM matrix. Int J Artif Intelligence Soft Comput. 2013;3(3):276–296.
   Google Scholar
• Virmani J, Kumar V, Kalra N, et al. Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound. Int J Convergence Comput. 2013;1(1):19–37.
   Google Scholar
• Xian G-M. An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Syst Appl. 2010;37(10):6737–6741.
   Web of Science ®Google Scholar
• Virmani J, Kumar V, Kalra N, et al. A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound. J Med Eng Technol. 2013;37(4):292–306.
   PubMedGoogle Scholar
• Goh V, Sanghera B, Wellsted DM, et al. Assessment of the spatial pattern of colorectal tumour perfusion estimated at perfusion CT using two-dimensional fractal analysis. Eur Radiol. 2009;19(6):1358–1365.
   PubMed Web of Science ®Google Scholar
• Cui C, Cai H, Liu L, et al. Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging. Eur Radiol. 2011;21(11):2318–2325.
   PubMed Web of Science ®Google Scholar
• Wibmer A, Hricak H, Gondo T, 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. Eur Radiol. 2015;25(10):2840–2850.
   PubMed Web of Science ®Google Scholar
• Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35(8):1798–1828.
   PubMed Web of Science ®Google Scholar
• LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444.
   PubMed Web of Science ®Google Scholar