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  1. Vani, N., Swomya, A., & Jayamma, N. (2017). MRI Brain tumor classification using support vector machineInternational Research Journal of Engineering and Technology, 1724-1729. doi: 10.1109/SCEECS.2014.6804439

  2. Beichel, R.R., Smith, B.J., Bauer, C., Ulrich, E.J., Ahmadvand, P., Budzevich, M.M., Gillies, R.J., Goldgof, D., Grkovski, M., Hamarneh, G., Huang, Q., Kinahan, P.E., Laymon, C.M., Mountz, J.M., Muzi, J.P., Muzi, M., Nehmeh, S., Oborski, M.J., Tan, Y., Zhao, B., Sunderland, J.J., Buatti, J.M. (2017). Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data. Med. Phys. 44(2); 479-496. doi: 10.1002/mp.12041
  3. Vallières, M., Kay-Rivest, E., Perrin, L.J., Liem, X., Furstoss, C., Aerts, H.J.W.L., Khaouam, N., Nguyen-Tan, P.F., Want, C.-S., Sultanem, K., Seuntjens, J., Naqa, I.E. (2017). Radiomics strategies for risk assessment of tumour failure in head-and-neck cancerScientific Reports, (arXiv 1703.08516)
  4. Paredes, D., Saha, A., Mazurowski, M.A.(2017). Deep learning for segmentation of brain tumors: can we train with images from different institutions? SPIE Medical Imaging: Computer-Aided Diagnosis, doi: 10.1117/12.2255696
  5. Kumar, S., Dharun. (2017). Combination of fuzzy c-means clustering and texture pattern matrix for brain MRI segmentation. Biomedical Research, 28(5) 
  6. Nabizadeh N, Kubat M. Automatic Tumor Segmentation in Single-Spectral MRI Using A Texture-Based and Contour-Based Algorithm. ScienceDirect, 77: 1-10. doi: 10.1016/j.eswa.2017.01.036
  7. Kaur, T., Saini, B.S., Gupta, S. (2016). A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images. Neural Computing and Applications. 1-24. doi: 10.1007/s00521-016-2751-4

  8. Song, J., Liu, Z., Zhong, W., Huang, Y., Ma, Z., Dong, D., Liang, C., Tian, J. (2016). Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis. Scientific Reports. 6:38282:1-9. doi: 10.1038/srep38282

  9. Crawford, L., Monod, A., Chen, A.X., Mukherjee, S., Rabadán, R. (2016). Topological Summaries of Tumor Images Improve Prediction of Disease Free Survival in Glioblastoma Multiforme. arXiv preprint arXiv:161106818

  10. Korfiatis, P., Kline, T.L., Erickson, B.J. (2016). Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning. J Tomography, 2(4) 334-340 DOI: 10.18383/j.tom.2016.00166
  11. Zheng, C., Wang, X., Feng, D. (Eds.). (2016). Topology guided demons registration with local rigidity preservation. 2016 IEEE 38th Annual International Conference Engineering in Medicine and Biology Society (EMBC). IEEEdoi: 10.1109/EMBC.2016.7590913

  12. Kotrotsou, A., Zinn, P.O., Colen, R.R. (2016). Radiomics in Brain Tumors: An Emerging Technique for Characterization of Tumor Environment. Magnetic Resonance Imaging Clinics of North America. 24(4); 719-29. doi: 10.1016/j.mric.2016.06.006

  13. Zhao, B., Tan, Y., Tsai, W.Y., Qi, J., Xie, C., Lu, L., Schwartz, L.H. (2016). Reproducibility of radiomics for deciphering tumor phenotype with imaging. Scientific Reports. 6:23428. doi: 10.1038/srep23428
  14. Li, H., Zhu, Y., Burnside, E.S., Huang, E., Drukker, K., Hoadley, K.A., Fan, C., Conzen, S.D., Zuley, M., Net, J.M., Sutton, E., Whitman, G.J., Morris, E., Perou, C.M., Ji, Y., Giger, M.L. (2016). Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. npj Breast Cancer. doi: 10.1038/npjbcancer.2016.12
  15. Grossmann, P., Gutman, D.A., Dunn Jr., W.D., Holder, C.A., Aerts, H.J.W.L. (2016). Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma. BMC Cancer. 16(611). doi: 10.1186/s12885-016-2659-5
  16. Zhu, Y., Li, H., Guo, W., Drukker, K., Lian, L., Giger, M.L., Ji, Y. (2015). Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Scientific Reports. 5(17787). doi: 10.1038/srep17787 
  17. Rajakumar, K., Muttan, S., Deepa, G., Revathy, S., Priya, B.S. (2015). Intelligent texture feature extraction and indexing for MRI image retrieval using curvelet and PCA with HTF. Advances in Natural and Applied Sciences. 9(6 SE) 506-513. doi: (link)
  18. Parmar, C., Leijenaar, R.T. Leijenaar, et al. H., Grossmann, P., Valazquez, E.R., Bussink, J., Rietveld, D., Rietbergen, M.M., Haibe-Kains, B., Lambin, P., Aerts, H.J.W.L. (2015). "Radiomic feature clusters and Prognostic Signatures specific for Lung and Head &Neck cancer." Sci Rep 5: 11044. Scientific Reports. 5(11044) doi: 10.1038/srep11044

  19.  Parmar, C., Grossmann, P. Grossmann, et al. Bussink, J., Lambin, P., Aerts, H.J.W.L. (2015). "Machine Learning methods for Quantitative Radiomic Biomarkers." Sci Rep 5: 13087.Tanougast C, Chaddad A.  Scientific Reports, 5(13087). doi: 10.1038/srep13087 
  20. Chaddad, A., Tanougast, C. (2015), High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Adv Advances in Bioinformatics, 15(2015728164). (link)doi: 10.1155/2015/728164
  21. Chaddad A. Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models International Journal of Biomedical Imaging, 2015. (link)
  22. Dhara AK, Mukhopadhyay S, Khandelwal N. 3d texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images. SPIE 2013. (link)
  23. Dhara AK, Mukhopadhyay S, Alam N, Khandelwal N. Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images. Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700K. (link)