Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications
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[1] W J Niessen,et al. Heterogeneity in DCE-MRI parametric maps: a biomarker for treatment response? , 2011, Physics in medicine and biology.
[2] M. Knopp,et al. Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.
[3] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[4] N. Just,et al. Improving tumour heterogeneity MRI assessment with histograms , 2014, British Journal of Cancer.
[5] Glen R Morrell,et al. Pharmacokinetic mapping for lesion classification in dynamic breast MRI , 2010, Journal of magnetic resonance imaging : JMRI.
[6] Wei Huang,et al. The magnetic resonance shutter speed discriminates vascular properties of malignant and benign breast tumors in vivo , 2008, Proceedings of the National Academy of Sciences.
[7] P. Tofts. Modeling tracer kinetics in dynamic Gd‐DTPA MR imaging , 1997, Journal of magnetic resonance imaging : JMRI.
[8] L. Costaridou,et al. Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis. , 2010, The British journal of radiology.
[9] A. Padhani. Dynamic contrast‐enhanced MRI in clinical oncology: Current status and future directions , 2002, Journal of magnetic resonance imaging : JMRI.
[10] Jack A Tuszynski,et al. Automatic prediction of tumour malignancy in breast cancer with fractal dimension , 2016, Royal Society Open Science.
[11] Joseph Naor,et al. Multiple Resolution Texture Analysis and Classification , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Kee Tung. Wong,et al. Texture features for image classification and retrieval. , 2002 .
[13] Carlo Sansone,et al. Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review , 2016, Journal of Medical and Biological Engineering.
[14] K. Berbaum,et al. Large-core breast biopsy: abnormal salivary cortisol profiles associated with uncertainty of diagnosis. , 2009, Radiology.
[15] Xiangyu Yang,et al. Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review , 2011, Journal of biomedicine & biotechnology.
[16] Ron Kikinis,et al. Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. , 2014, Translational oncology.
[17] N. Thacker,et al. Quantifying heterogeneity in human tumours using MRI and PET. , 2012, European journal of cancer.
[18] R. Kamal,et al. Breast imaging in the young: the role of magnetic resonance imaging in breast cancer screening, diagnosis and follow-up. , 2013, Journal of thoracic disease.
[19] J W Baish,et al. Fractals and cancer. , 2000, Cancer research.
[20] Michal Strzelecki,et al. Texture Analysis Methods - A Review , 1998 .
[21] Sachin N Prasad,et al. The role of various modalities in breast imaging. , 2007, Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia.
[22] J P B O'Connor,et al. Dynamic contrast-enhanced imaging techniques: CT and MRI. , 2011, The British journal of radiology.
[23] Christoph I. Lee,et al. Breast cancer screening: an evidence-based update. , 2015, The Medical clinics of North America.
[24] Wei Huang,et al. Dynamic NMR effects in breast cancer dynamic-contrast-enhanced MRI , 2008, Proceedings of the National Academy of Sciences.
[25] H. Dvorak,et al. Heterogeneity of the Tumor Vasculature , 2010, Seminars in thrombosis and hemostasis.
[26] S. Bondari,et al. The Role of Imaging Techniques in Diagnosis of Breast Cancer , 2012 .
[27] Giorgio Bianciardi,et al. Fractals and Pathology , 2016 .