About MIVIC is a research group in the Division of Physics, Engineering, Mathematics and Computer Science in Delaware State University. MIVIC's research interests include medical image analysis, mathematical methods for visual computing, machine learning, and visualization. More information about the group's research and training activities, publications, and group members can be found below: Research
Subspace Analysis for Multi-temporal Disaster Mapping Using Satellite Imagery Submitted by admin on Fri, 12/16/2022 - 10:54 A. M. Okorie and Makrogiannis, S., “Subspace Analysis for Multi-temporal Disaster Mapping Using Satellite Imagery”, in Advances in Visual Computing, Cham, 2022.
Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography Submitted by admin on Fri, 12/16/2022 - 10:49 S. Makrogiannis, Okorie, A., Di Iorio, A., Bandinelli, S., and Ferrucci, L., “Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography”, Frontiers in physiology, vol. 13, p. 951368, 2022.
Editorial for “Cross-Cohort Automatic Knee MRI Segmentation with Multi-Planar U-Nets” Submitted by admin on Fri, 09/02/2022 - 10:50 S. Makrogiannis, “Editorial for “Cross-Cohort Automatic Knee MRI Segmentation with Multi-Planar U-Nets””, Journal of Magnetic Resonance Imaging, vol. 55, pp. 1664-1665, 2022.
Sparse Analysis of Block-Boosted Deep Features for Osteoporosis Classification Submitted by admin on Fri, 09/02/2022 - 10:45 C. E. Harris and Makrogiannis, S., “Sparse Analysis of Block-Boosted Deep Features for Osteoporosis Classification”, in 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2022.
Label efficient segmentation of single slice thigh CT with two-stage pseudo labels Submitted by admin on Wed, 05/25/2022 - 20:58 Q. Yang, Yu, X., Lee, H. Hin, Tang, Y., Bao, S., Gravenstein, K. S., Moore, A. Zenobia, Makrogiannis, S., Ferrucci, L., and Landman, B. A., “Label efficient segmentation of single slice thigh CT with two-stage pseudo labels”, Journal of Medical Imaging, vol. 9, p. 052405, 2022.
Quantification of muscle, bones, and fat on single slice thigh CT Submitted by admin on Wed, 05/25/2022 - 20:57 Q. Yang, Yu, X., Lee, H. Hin, Tang, Y., Bao, S., Gravenstein, K. S., Moore, A. Zenobia, Makrogiannis, S., Ferrucci, L., and Landman, B. A., “Quantification of muscle, bones, and fat on single slice thigh CT”, in Medical Imaging 2022: Image Processing, 2022.
Simulation of mid-thigh anatomy for virtual clinical studies Submitted by admin on Wed, 05/25/2022 - 20:56 A. Okorie, Bakic, P., and Makrogiannis, S., “Simulation of mid-thigh anatomy for virtual clinical studies”, in Medical Imaging 2022: Physics of Medical Imaging, 2022.
TIDAQUNET: tissue identification and quantification network for mid-thigh CT segmentation Submitted by admin on Wed, 05/25/2022 - 20:56 S. Makrogiannis, Annasamudram, N., and Biswas, T., “TIDAQUNET: tissue identification and quantification network for mid-thigh CT segmentation”, in Medical Imaging 2022: Image Processing, 2022.
Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms Submitted by admin on Wed, 12/15/2021 - 10:08 S. Makrogiannis, Zheng, K., and Harris, C., “Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms”, Frontiers in Oncology, 2021.
