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  1. Chaddad, A., Sabri, S., Niazi, T., & Abdulkarim, B. (2018). Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. Medical & Biological Engineering & Computing, 1-14. doi:10.1007/s11517-018-1858-4

  2. Drukker, K., Li, H., Antropova, N., Edwards, A., Papaioannou, J., & Giger, M. L. (2018). Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival "early on" in neoadjuvant treatment of breast cancer. Cancer Imaging, 18(1). doi:10.1186/s40644-018-0145-9

  3. Reeves, A. P., Xie, Y., & Liu, S. (2018). Automated image quality assessment for chest CT scans. Medical Physics, 45(2), 561-578. DOI: 10.1002/mp.12729

  4. AlBadawy, E. A., Saha, A., & Mazurowski, M. A. (2018). Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Medical Physics. DOI: 10.1002/mp.12752

  5. Larue, R. T. H. M., Van De Voorde, L., van Timmeren, J. E., Leijenaar, Ralph T. H., Berbee, M., Sosef, M. N., Schreurs, W. M. J., van Elmpt, W., Lambin, P. (2017). 4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers. Radiotherapy and Oncology. DOI: 10.1016/j.radonc.2017.07.023

  6. Sutton, E. J., Huang, E. P., Drukker, K., Burnside, E. S., Li, H., Net, J. M., Rao, A., Whitman, G. J., Zuley, M., Ganott, M., Bonaccio, E., Giger, M. L., Morris, E. A. (2017). Breast MRI radiomics: Comparison of computer- and human-extracted imaging phenotypes. European Radiology Experimental. DOI: 10.1186/s41747-017-0025-2

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

  8. 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
  9. 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 cancer. Scientific Reports, (arXiv 1703.08516)
  10. 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
  11. Kumar, S., Dharun. (2017). Combination of fuzzy c-means clustering and texture pattern matrix for brain MRI segmentation. Biomedical Research, 28(5) 
  12. 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

  13. 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

  14. 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

  15. 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. 

  16. 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

  17. 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). IEEE. DOI: 10.1109/EMBC.2016.7590913

  18. 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

  19. 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
  20. 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
  21. 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
  22. 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 
  23. 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)

  24. Parmar, C., Leijenaar, R.T.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.Scientific Reports. 5(11044) DOI: 10.1038/srep11044

  25.  Parmar, C., Grossmann, P., Bussink, J., Lambin, P., Aerts, H.J.W.L. (2015). Machine Learning methods for Quantitative Radiomic Biomarkers. Scientific Reports, 5(13087). DOI: 10.1038/srep13087 
  26. Chaddad, A., Tanougast, C. (2015), High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Advances in Bioinformatics, 15(728164). DOI: 10.1155/2015/728164
  27. Chaddad, A. (2015). Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models.  International Journal of Biomedical Imaging, 2015(868031). DOI: 10.1155/2015/868031
  28. Dhara, A.K., Mukhopadhyay, S., Khandelwal, N. (2013). 3d texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images. Medical Imaging 2013: Computer-Aided Diagnosis, 8670. DOI: 10.1117/12.2007016

  29. Dhara, A.K., Mukhopadhyay, S., Alam, N., Khandelwal, N. (2013). Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images. Medical Imaging 2013: Computer-Aided Diagnosis, 8670. DOI: 10.1117/12.2006970

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  1. Edalati-rad, A., & Mosleh, M. (2018). Improving brain tumor diagnosis using MRI segmentation based on collaboration of beta mixture model and learning automata. Arabian Journal for Science and Engineering, 1-13. doi:10.1007/s13369-018-3320-1

  2. Taghanaki, S. A., Duggan, M., Ma, H., Hou, X., Celler, A., Benard, F., Hamarneh, G. (2017). Segmentation-free direct tumor volume and metabolic activity estimation from PET scans. Computerized Medical Imaging and Graphics, 63, 53-56. DOI: 10.1016/j.compmedimag.2017.12.004

  3. Y Ren, J Ma, J Xiong, Y Chen, L Lu, J Zhao (2018) Improved False Positive Reduction by Novel Morphological Features for Computer-Aided Polyp Detection in CT Colonography. IEEE Journal of Biomedical and Health Informatics. DOI:10.1109/JBHI.2018.2808199

  4. Babu, J. S., Mathew, S., & Simon, R. (2017). Biomedical image retrieval using LBWP. International Research Journal of Engineering and Technology (IRJET), 4(9), 839-843. https://www.irjet.net/archives/V4/i9/IRJET-V4I9147.pdf

  5. Hostetter, J. M., Morrison, J. J., Morris, M., Jeudy, J., Wang, K. C., & Siegel, E. (2017). Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset. Journal of the American Medical Informatics Association, 24(6), 1046-1051. DOI:10.1093/jamia/ocx012

  6. Mason J, Perelli A, Nailon W, Davies M. (2017) Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT? arXiv 1703.07179

  7. Hsieh KL-C, Tsai R-J, Teng Y-C, Lo C-M. Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI. PloS one. 2017;12(2):e0171342 (link)

  8. Hsieh KL-C, Lo C-M, Hsiao C-J. Computer-aided grading of gliomas based on local and global MRI features. Computer Methods and Programs in Biomedicine. 2017;139:31-8. DOI: 10.1016/j.cmpb.2016.10.021

  9. Yang H, Liu F, Wang Z, Tang H, Sun S, Sun S. Research on the Content-Based Classification of Medical Image. Journal of Medical Imaging and Health Informatics. 2017;7(1):129-36. (link)

  10. Rezaie AA, Habiboghli A. Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation. International Journal of Interactive Multimedia and Artificial Inteligence. 2017;4(Special Issue on 3D Medicine and Artificial Intelligence):15-9. (link)

  11. Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, Wang G. Low-dose CT via convolutional neural network. Biomedical Optics Express. 2017;8(2):679-94.(link)

  12. Vallières M, Freeman C, Skamene S, El Naqa I. 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. 2015;60(14):5471.
  13. Kazdal S, Dogan B, Camurcu AY, editors. Computer-aided detection of brain tumors using image processing techniques. Signal Processing and Communications Applications Conference (SIU), 2015 23th; 2015: IEEE.
  14. Gupta A, Martens O, Le Moullec Y, Saar T, editors. A tool for lung nodules analysis based on segmentation and morphological operation. Intelligent Signal Processing (WISP), 2015 IEEE 9th International Symposium on; 2015: IEEE.
  15. Benninghoff H, Garcke H. Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes. arXiv:1506.07136. 2015.
  16. Zabala-Travers S, Choi M, Cheng W-C, Badano A. Effect of color visualization and display hardware on the visual assessment of pseudocolor medical images. Medical Physics. 2015;42(6):2942-54.
  17. Guvenis A, Koc A. OPTIMISING DELINEATION ACCURACY OF TUMOURS IN PET FOR RADIOTHERAPY PLANNING USING BLIND DECONVOLUTION. Radiation Protection Dosimetry. 2015:ncv110.
  18. Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H, Velazquez ER, Lambin P, Gu Y, Balagurunathan Y. Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma. PloS one. 2015;10(3).
  19. Buerger C, Sénégas J, Kabus S, Carolus H, Schulz H, Agarwal H, Turkbey B, Choyke P, Renisch S. Comparing nonrigid registration techniques for motion corrected MR prostate diffusion imaging. Medical physics. 2015;42(1):69-80.
  20. Abedini M, Codella N, Connell J, Garnavi R, Merler M, Pankanti S, Smith J, Syeda-Mahmood T. A generalized framework for medical image classification and recognition. IBM Journal of Research and Development. 2015;59(2/3):1:18.
  21. Blessy SPS, Sulochana CH. Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation. Technology and Health Care. 2014.
  22. ElNawasany AM, Ali AF, Waheed ME. A Novel Hybrid Perceptron Neural Network Algorithm for Classifying Breast MRI Tumors.  Advanced Machine Learning Technologies and Applications: Springer; 2014. p. 357-66.
  23. Hong S, Huang Y, Cao Y, Chen X, Han J-DJ. Approaches to uncovering cancer diagnostic and prognostic molecular signatures. Molecular & Cellular Oncology. 2014.
  24. Codella N, Connell J, Pankanti S, Merler M, and Smith JR. Automated Medical Image Modality Recognition by Fusion of Visual and Text Information. Medical Image Computing and Computer-Assisted Intervention. 2014, Springer. 487-495. (link)
  25. Ertugrul OF. Adaptive Texture Energy Measure Method. International Journal of Intelligent Information Systems. 2014. 3(2):13-18. doi:10.11648/j.ijiis.20140302.11 (link)
  26. Kawa J, Juszczyk J, Pyciński B, Badura P, Pietka E. Radiological Atlas for Patient Specific Model Generation. Information Technologies in Biomedicine, 2014 4:69-82. 10.1007/978-3-319-06596-0_7. (link)
  27. Kowalik-Urbaniak I, Brunet D, Wang J, Koff D, Smolarski-Koff N, Vrscay ER, Wallace B, Wang Z. The quest for ‘diagnostically lossless’ medical image compression: a comparative study of objective quality metrics for compressed medical images. SPIE Medical Imaging. 2014. Vol. 9073. International Society for Optics and Photonics. doi:10.1117/12.2043196 (link)
  28. Naresh P and Shettar R. Image Processing and Classification Techniques for Early Detection of Lung Cancer for Preventive Health Care: A Survey. International Journal of Recent Trends in Engineering & Technology, 2014. 11:595-601 (link)
  29. Patel NP, Parmar SK, and Jain KR. Swift Pre Rendering Volumetric Visualization of Magnetic Resonance Cardiac Images based on Isosurface Technique. Procedia Technology, 2014. 14:422-429. doi:10.1016/j.protcy.2014.08.054 (link)
  30. Roy S, Brown MS, and Shih GL. Visual Interpretation with Three-Dimensional Annotations (VITA): Three-Dimensional Image Interpretation Tool for Radiological Reporting. Journal of Digital Imaging, 2014. 27(1):49-57. doi: 10.1007/s10278-013-9624-5 (link)

  31. Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S, Liu J, Turkbey E, Summers RM. A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations.  Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: Springer; 2014. p. 520-7.

  32. Sivakumar S, and Chandrasekar C. A Study on Image Denoising for Lung CT Scan Images.International Journal of Emerging Technologies in Computational and Applied Sciences, 2014. 7(1):86-91 (link)

  33. Seff A, Lu L, Cherry KM, Roth HR, Liu J, Wang S, Hoffman J, Turkbey EB, Summers RM. 2d view aggregation for lymph node detection using a shallow hierarchy of linear classifiers.  Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: Springer; 2014. p. 544-52.

  34. Harmon S, Wendelberger B, and Jeraj R. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis. Medical Physics, 2014. 41(6): p. 178-178. doi:10.1118/1.4888150 (link)
  35. Krishnakumar V. and Parthiban L. Performance Analysis of Denoising in MR Images with Double Density Dual Tree Complex Wavelets, Curvelets and NonSubsampled Contourlet Transforms. Annual Review & Research in Biology, 2014. 4(19):2938-2956. doi:10.9734/ARRB/2014/9131#sthash.qFePVdL1.dpuf (link)
  36. Codella N, Merler M. IBM TJ Watson Research Center. Semantic Model Vector for ImageCLEF2013. June 18, 2014. (link)
  37. Agostinelli F, Anderson MR, and Lee H. Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising. Advances in Neural Information Processing Systems. 2013. (link)

  38. Agostinelli F, Anderson MR, Lee H, editors. Robust Image Denoising with Multi-Column Deep Neural Networks. Advances in Neural Information Processing Systems; 2013.

  39. Breseman K, Lee C, Bloch BN, and Jaffe C. Constructing 3D-Printable CAD Models of Prostates from MR Images. Bioengineering Conference (NEBEC),
    39th Annual Northeast , IEEE, 27-28. 5-7 April 2013. doi:10.1109/NEBEC.2013.8
  40. Buckler A, Liu TT, Savig E, Suzek BE, Rubin DL, and Paik D. Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers. Journal of Digital Imaging, 2013. 26(4):630-641. doi:10.1007/s10278-013-9599-2 (link)
  41. Heyns M, Breseman K, Lee C, Bloch BN, Jaffe C, and Xiang H. Design of a Patient-Specific Radiotherapy Treatment Target. Bioengineering Conference (NEBEC), 2013 39th Annual Northeast. 2013.171-172. IEEE. doi:10.1109/NEBEC.2013.75
  42. Kumar A, Kim J, Cai W, Fulham M, and Feng D. Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data. Journal of Digital Imaging, 2013. 26(6):1025-1039. doi: 10.1007/s10278-013-9619-2.(link)
  43. Lundström C. vPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging. International Journal of Computer Assisted Radiology and Surgery, 2013. 8(3):437-450. doi: 10.1007/s11548-012-0792-4 (link)
  44. Olmedo I, Guerra Perez Y, Johnson JF, Raut L, Hoe DHK. Image segmentation on GPGPUs: a cellular automata-based approach. Proceedings of the 2013 Summer Computer Simulation Conference. Society for Modeling & Simulation International. 2013. 51. (link)
  45. Pambrun JF, Noumeir R. Compressibility variations of JPEG2000 compressed computed tomography. Conference Proceedings, 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013:3375-3378. doi: 10.1109/EMBC.2013.6610265 (link)
  46. Roozgard A, Barzigar N, Verma P, and Cheng S. 3D medical image denoising using 3D block matching and low-rank matrix completion. Signals, Systems and Computers, Asilomar Conference, 3-6 Nov. 2013, 253 – 257 IEEE. doi:10.1109/ACSSC.2013.6810271
  47. Yankeelov TE, Atuegwu N, Hormuth D, et al. Clinically Relevant Modeling of Tumor Growth and Treatment Response. Sci Transl Med. 2013 May 29;5(187):187ps9 doi: 10.1126/scitranslmed.3005686 (link) .
  48. Huang LC, Yseng LY, Hwang MS. A reversible data hiding method by histogram shifting in high quality medical images. Journal of Systems and Software 2013 March;86(3):716-27 doi: 10.1016/j.jss.2012.11.024 (link)
  49. Pheng HS and Shamsuddin SM. Texture classification of lung computed tomography images. 2012 International Conference on Graphic and Image Processing. 2013. Vol. 8768. International Society for Optics and Photonics. doi:10.1117/12.2011108 (link)
  50. Barzigar N, Roozgard A, Verma P, Cheng S. Removing Mixture Noise from Medical Images Using Block Matching Filtering and Low-Rank Matrix Completion. Healthcare Informatics, Imaging and Systems Biology, IEEE International Conference. 2012.134. doi:10.1109/HISB.2012.59 (link)
  51. Otake Y, Schafer S, Stayman JW, Zbijewski W, Kleinszig G, Graumann R, Khanna AJ, Siewerdsen JH. Automatic localization of target vertebrae in spine surgery using fast CT-to-fluoroscopy (3D-2D) image registration. SPIE Medical Imaging, 2012. Volume: 8316. International Society for Optics and Photonics. doi:10.1117/12.911308 (link)
  52. Roozgard A, Cheng AS, Liu H. Malignant nodule detection on lung ct scan images with kernel rx-algorithm. Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on 5-7 Jan. 2012 499 – 502. IEEE. doi: 10.1109/BHI.2012.6211627.
  53. Biancardi AM, Jirapatnakul AC, Reeves AP. A comparison of ground truth estimation methods. International Journal of Computer Assisted Radiology and Surgery, 2010. 5(3):295-305. doi: 10.1007/s11548-009-0401-3 (link)
  54. Soysal OM, Chen P, Schneider H. An Image Processing Tool for Efficient Feature Extraction in Computer-Aided Detection Systems. Granular Computing (GrC) IEEE International Conference 2010. 14-16 Aug. 438-442. doi:10.1109/GrC.2010.128
  55. Tseng LY and Huang LC. Automatic fissure detection in CT images based on the genetic algorithm. Machine Learning and Cybernetics (ICMLC), International Conference. IEEE. 2010. 5: 2583 – 2588. doi: 10.1109/ICMLC.2010.5580871

  56. Kumar, D., A. Wong, et al. (2015). Lung Nodule Classification Using Deep Features in CT Images. Computer and Robot Vision (CRV), 2015 12th Conference on, IEEE.

  57. Kanas, V. G., E. I. Zacharaki, et al. (2015). "A low cost approach for brain tumor segmentation based on intensity modeling and 3D Random Walker." Biomedical Signal Processing and Control 22: 19-30.

  58. Magdy, E., N. Zayed, et al. (2015). "Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features." International Journal of Biomedical Imaging 2015.

  59. Zayed, N. and H. A. Elnemr (2015). "Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities." International Journal of Biomedical Imaging 2015.

  60. Chaddad, A. and C. Tanougast "High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features." Advances in Bioinformatics 2015.  doi: 10.1155/2015/728164 (duplicate of #25 in RADIOMICS)
  61. Li M, Miller K, Joldes GR, Kikinis R, Wittek A. Biomechanical model for computing deformations for whole-body image registration: A meshless approach. International. Journal for Numerical Methods in Biomedical Engineering. 2016. doi: 10.1002/cnm.2771

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