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
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.
Spatially localized sparse approximations of deep features for breast mass characterization Submitted by admin on Mon, 08/28/2023 - 15:47 C. Harris, Okorie, U., and Makrogiannis, S., “Spatially localized sparse approximations of deep features for breast mass characterization”, Mathematical Biosciences and Engineering, vol. 20, pp. 15859-15882, 2023.
Live Cell Segmentation and Tracking Techniques Submitted by admin on Mon, 08/28/2023 - 15:40 Y. Wang, Williams, O., Annasamudram, N., and Makrogiannis, S., “Live Cell Segmentation and Tracking Techniques”, in Intelligent Video Analytics: Clustering and Classification Applications, E. Alfy Georg Zhou, Ed. CRC Press, 2023.
Editorial for "Diagnosis of Sarcopenia Using the L3 Skeletal Muscle Index Estimated from the L1 Skeletal Muscle Index on MR Images in Patients with Cirrhosis" Submitted by admin on Mon, 08/28/2023 - 15:37 S. Makrogiannis, “Editorial for "Diagnosis of Sarcopenia Using the L3 Skeletal Muscle Index Estimated from the L1 Skeletal Muscle Index on MR Images in Patients with Cirrhosis"”, Journal of Magnetic Resonance Imaging, 2023.
