Members: Chelsea Harris, Keni Zheng, Sokratis Makrogiannis
The analysis and characterization of imaging patterns is a significant research area with several applications to biomedicine, remote sensing, homeland security, social networking and numerous other domains. Some examples of the application computer-aided diagnosis, face recognition, object recognition, and biometrics. We study and develop mathematical methods and algorithms for computer aided-diagnosis.
We have work on diagnose the disease using texture characteristics that are derived from imaging modalities. Also the methods for calculation sparse representations to classify imaging patterns and we explore the advantages of this technique over traditional texture-based classification. Based on the classification system we have, the classification accuracy above 90% .
- , “Integrative blockwise sparse analysis for tissue characterization and classification”, Artificial Intelligence in Medicine, vol. 107, p. 101885, 2020.
- , “Ensembles of Sparse Classifiers for Osteoporosis Characterization in Digital Radiographs”, in SPIE Medical Imaging 2019: Computer-Aided Diagnosis, 2019.
- , “Differential Aging Signals in Abdominal CT Scans”, Academic Radiology, vol. 24, no. 12, pp. 1535 - 1543, 2017.
- , “Sparse Representation using Block Decomposition for Characterization of Imaging Patterns.”, Proceedings of 3rd International Workshop on Patch-based Techniques in Medical Imaging (2017). pp. 158-166, 2017.
- , “Image-Based Tissue Distribution Modeling for Skeletal Muscle Quality Characterization.”, IEEE Trans Biomed Eng, vol. 63, no. 4, pp. 805-13, 2016.
- , “A Pattern Recognition System for Bone Texture Characterization from Digital Radiography,”, IEEE International Conference on Engineering in Medicine and Biology. pp. 1034-1037, 2016.
- , “Computer-aided assessment of regional abdominal fat with food residue removal in CT.”, Acad Radiol, vol. 20, pp. 1413–1421, 2013.