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
Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy Submitted by admin on Thu, 07/24/2025 - 12:15 N. Annasamudram, Zhao, J., Oluwadare, O., Prashanth, A., and Makrogiannis, S., “Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy”, Scientific Reports, vol. 15, p. 11717, 2025.
Artificial intelligence in pediatric osteopenia diagnosis: evaluating deep network classification and model interpretability using wrist X-rays Submitted by admin on Thu, 07/24/2025 - 12:13 C. E. Harris, Liu, L., Almeida, L., Kassick, C., and Makrogiannis, S., “Artificial intelligence in pediatric osteopenia diagnosis: evaluating deep network classification and model interpretability using wrist X-rays”, Bone Reports, vol. 25, p. 101845, 2025.
Pediatric osteopenia prediction and interpretation in wrist x-rays Submitted by admin on Thu, 07/24/2025 - 12:12 C. E. Harris, Liu, L., and Makrogiannis, S., “Pediatric osteopenia prediction and interpretation in wrist x-rays”, in Medical Imaging 2025: Computer-Aided Diagnosis, 2025.
Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water–fat separated MRI Submitted by admin on Fri, 09/13/2024 - 09:14 N. V. Annasamudram, Okorie, A. M., Spencer, R. G., Kalyani, R. R., Yang, Q., Landman, B. A., Ferrucci, L., and Makrogiannis, S., “Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water–fat separated MRI”, Journal of Medical Imaging, vol. 11, p. 054003, 2024.
Evaluating breast density classification by sparse approximation classifiers and deep networks using simulated digital mammograms Submitted by admin on Wed, 08/28/2024 - 12:51 C. E. Harris, Okorie, U., Bakic, P. R., and Makrogiannis, S., “Evaluating breast density classification by sparse approximation classifiers and deep networks using simulated digital mammograms”, in Virtual Imaging Trials in Medicine, 2024.
PET Imaging of Neurofibromatosis Type 1 with a Fluorine-18 Labeled Tryptophan Radiotracer Submitted by admin on Wed, 08/28/2024 - 12:51 X. Yue, Stauff, E., Boyapati, S., Langhans, S. A., Xu, W., Makrogiannis, S., Okorie, U. J., Okorie, A. M., Kandula, V. V. R., Kecskemethy, H. H., Nikam, R. M., Averill, L. W., and Shaffer, T. H., “PET Imaging of Neurofibromatosis Type 1 with a Fluorine-18 Labeled Tryptophan Radiotracer”, Pharmaceuticals, vol. 17, p. 685, 2024.
Multi-method and multi-atlas segmentation fusion for delineation of thigh muscle groups in 3D water-fat separated MRI Submitted by admin on Wed, 08/28/2024 - 12:50 N. V. Annasamudram, Okorie, A. M., Spencer, R. G., Kalyani, R. R., Yang, Q., Landman, B. A., Ferrucci, L., and Makrogiannis, S., “Multi-method and multi-atlas segmentation fusion for delineation of thigh muscle groups in 3D water-fat separated MRI”, in Medical Imaging 2024: Image Processing, 2024.
Mammographic Breast Density Classification by Integration of Deep Dictionaries and Multi-Model Sparse Approximations Submitted by admin on Wed, 08/28/2024 - 12:49 C. Harris, Okorie, U., and Makrogiannis, S., “Mammographic Breast Density Classification by Integration of Deep Dictionaries and Multi-Model Sparse Approximations”, in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024.
A Joint Scale Analysis And Machine Learning Framework For Cell Detection And Segmentation In Time Lapse Microscopy Submitted by admin on Wed, 08/28/2024 - 12:48 N. Annasamudram and Makrogiannis, S., “A Joint Scale Analysis And Machine Learning Framework For Cell Detection And Segmentation In Time Lapse Microscopy”, in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024.