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Collection: CT Colonography

  1. Gayathri DK, Radhakrishnan R, Rajamani K. Segmentation of colon and removal of opacified fluid for virtual colonoscopy. Pattern Analysis and Applications. 2017:1-15. doi: 10.1007/s10044-017-0614-y

  2. Pang S, Yu Z, Orgun MA. A Novel End-to-End Classifier Using Domain Transferred Deep Convolutional Neural Networks for Biomedical Images. Computer Methods and Programs in Biomedicine. 2017. (link)
  3. Yahya-Zoubir B, Hamami L. et al. Automatic 3D Mesh-Based Centerline Extraction from a Tubular Geometry Form. Information Technology and Control, 2016. 45(2):156-163. (link)
  4. Alazmani A, Hood A, et al. Quantitative Assessment of Colorectal Morphology: Implications for Robotic Colonoscopy. Medical Engineering and Physics, 2016. 38(2):148-154. (link)
  5. Gayathri Devi K, Radhakrishnan R. Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network. Computational and Mathematical Methods in Medicine. 2015;2015.
  6. Namías R, et al., Automatic rectum limit detection by anatomical markers correlation. Computerized Medical Imaging and Graphics, 2014. 38(4):245-250.(link)
  7. Boone DJ, Halligan S, Roth HR, et al., CT Colonography: External Clinical Validation of an Algorithm for Computer-assisted Prone and Supine Registration. Radiology, 2013. 268(3):752-760.(link)
  8. Roth HR, et al., External clinical validation of prone and supine CT colonography registration in Abdominal Imaging. Computational and Clinical Applications 2012, Springer. 7601:10-19.(link)

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  1. Park SY and Sargent D. Tumor propagation model using generalized hidden Markov model. Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331G February 24, 2017); 10.1117/12.2254583
  2. Sargent D, Park S-Y. Semi-automatic 3D lung nodule segmentation in CT using dynamic programming. Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332R (February 24, 2017) doi: 10.1117/12.2254575
  3. Nishio M, Nagashima C. Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity. Academic Radiology. 2017;24(3):328-36. (link)

Collection: TCGA-BRCA

  1. AL-Dabagh MZ, AL-Mukhtar FH. Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine. IJAERS. 2017;4(3):258-63. doi: 10.22161/ijaers.4.3.41
  2. Angela Giardino, Supriya Gupta, Emmi Olson, Karla Sepulveda, Leon Lenchik, Jana Ivanidze, Rebecca Rakow-Penner, Midhir J. Patel, Rathan M. Subramaniam, Dhakshinamoorthy Ganeshan. Role of Imaging in the Era of Precision Medicine. Academic Radiology, Available online 25 January 2017 doi: 10.1016/j.acra.2016.11.021
  3. Albiol, Alberto; Corbi, Alberto; Albiol, Francisco. Automatic intensity windowing of mammographic images based on a perceptual metric. Medical Physics, 2473-4209.10.1002/mp.12144 
  4. Wu, J; Sun, X; Wang, J; Cui, Y;  Kato, F; Shirato, H; Ikeda, DM.; Li, R. Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation. Journal of Magnetic Resonance Imaging, 2586 doi: 10.1002/jmri.25661

  5. Wu J, Cui Y, Sun X, Cao G, Li B, Ikeda DM, Kurian AW, Li R. Unsupervised clustering of quantitative image phenotypes reveals breast cancer subtypes with distinct prognoses and molecular pathways. Clinical Cancer Research. 2017:clincanres. 2415.016. (link)

  6. Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging. Radiology, 2014. DOI: 10.1148/radiol.14132641 (link)

  7. Lavasani, S. N., A. F. Kazerooni, et al. (2015). Discrimination of Benign and Malignant Suspicious BreastTumors Based on Semi-Quantitative DCE-MRI ParametersEmploying Support Vector Machine. Frontiers in Biomedical Technologies 2(2): 397-403.

  8. Anand, S., V. Vinod, et al. Application of Fuzzy c-means and Neural networks to categorize tumor affected breast MR Images. International Journal of Applied Engineering Research 10(64): 2015.

  9. Guo, W., H. Li, et al. (2015). Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. Journal of Medical Imaging 2(4): 041007-041007.

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