Medical Image Analysis Techniques for Computer-Aided Diagnosis and Tissue Characterization

Members: 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% [1,2].

[1]K. Zheng and S. Makrogiannis, "Bone texture characterization for osteoporosis diagnosis using digital radiography," in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Aug 2016, pp. 1034-1037.

[2]K. Zheng and S. Makrogiannis, "Sparse representation using block decomposition for characterization of imaging patterns," in Patch-Based Techniques in Medical Imaging: Third International Workshop, Patch-MI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings, G. Wu, B. C. Munsell, Y. Zhan, W. Bai, G. Sanroma, and P. Coupe, Eds. Cham: Springer International Publishing, 2017, pp. 158-166.