Position/s

Lecturer
School of Computer Science, Engineering and Mathematics

Biography

Mariusz Bajger received the M.Sc. in Applied Mathematics degree from the Jagiellonian University in Cracow (Poland) in 1988 and the PhD degree in mathematics from the University of Queensland in 1996. Since 2002 he is a full-time lecturer with the School of Computer Science, Engineering and Mathematics at the Flinders University. His research interests include applications of mathematics and computer science to problems in medical image analysis and pattern recognition with focus on breast cancer detection in screening mammograms, whole-body CT segmentation and computational human anatomy,

Qualifications

PhD Mathematics, University of Queensland, Australia, 1996.
MSc Applied Mathematics, Jagiellonian University, Poland, 1988.

Honours, awards and grants

  • Channel 7 Children's Research Foundation Grant, 2012-2013
  • Lyn Wrigley Breast Cancer Research & Development Fund, Breast Cancer Research Grant, 2011
  • Lyn Wrigley Breast Cancer Research & Development Fund, Breast Cancer Research Grant, 2010
  • Lyn Wrigley Breast Cancer Research & Development Fund, Breast Cancer Research Grant, 2009
  • National Breast Cancer Foundation Grant 2006-2008
  • Lyn Wrigley Breast Cancer Research & Development Fund, Breast Cancer Research Grant, 2007
  • 3rd prize in M. Kuczma's competition for the best paper in Functional Equations written by Polish author in 2004.

Key responsibilities

  • Deputy Director of Study (Information Technology and Software Engineering)
  • Coordinator of Honours in Computer Science/IT 1 year Program

Topic Coordinator:

Topic Lecturer:

Research expertise

  • Artificial intelligence and image processing

Research interests

Medical Image Analysis, mammography, image processing, pattern recognition.

Supervisory interests

  • Medical image analysis

RHD research supervision

Current

Associate supervisor : Medical Image Analysis (1) ; Mammography (1) ;

Completion

Associate supervisor : Medical Image Analysis (1) ; Mathematical Modeling (1) ;

Publications

  • Omondi, A.R., Rajapakse, J.C. and Bajger, M. (2006). FPGA Neurocomputers. In Amos Omondi, Jagath Rajapakse, ed. FPGA Implementations of Neural Networks. Dordrecht, The Netherlands: Springer, pp. 1-36.
    [10.1007/0-387-28487-7_1]
  • Lee, G. and Bajger, M. (2014). Application of statistical inference in medical images. In Proceedings of ANZIAM 2014. ANZIAM 2014 Conference. Rotorua, New zealand. Feb 2014.
  • Lee, G., Bajger, M. and Caon, M. (2014). FBIseg tool for the visible human project. In CARS 2014 Proceeding of the 28th International Congress and Exhibition in International Journal of Computer Assisted Radiology and Surgery. Springer. 28th International Congress and Exhibition on Computer Assisted Radiology and Surgery. Fukuoka, Japan. Jun 2014, pp. 40-41.
  • Lee, G. and Bajger, M. (2013). Statistical inference and medical image segmentation. In Proceedings of the 49th ANZIAM Conference. ANZIAM Conference 2013.
  • Sidik, W.A., Bottema, M. and Bajger, M. (2011). A Cross-Species Avian-Human Influenza Epidemic Model: Transport related co-infection. In Proceedings of 7th ICIAM Conference. Canada7th ICIAM Conference 2011. Vancouver, Canada. Jul 2011.
  • Sidik, W.A., Bottema, M. and Bajger, M. (2011). A Cross-Species Avian-Human Influenza Epidemic Model: Effects of human behaviours on the disease spread and control. In Proceedings of the 6th SEAMS-GMU International Conference on Mathematics and Applications. 6th SEAMS-GMU Conference. Jogjakarta. Jul 2011.
  • Sidik, W.A., Bottema, M. and Bajger, M. (2011). A Cross-Species Avian-Human Influenza Epidemic Model: Economic trade off on the disease spread and controls. In Proceedings of AICST Conference. ACIKITA International Conference of Science and Technology. Jakarta, Indonesia. Jul 2011.
  • Sidik, W.A., Bottema, M. and Bajger, M. (2011). A Cross-Species Avian Human Influenza Epidemic Model: Effects of some control strategies. In Proceedings of ANZIAM Conference. ANZIAM Conference 2011. Adelaide. Jan 2011.
  • Lee, G., Bajger, M. and Caon, M. (2011). CAnat: An Algorithm for the Automatic Segmentation of Anatomy of Medical Images. In EPSM-ABEC 2011 Conference. Australasian Physical & Engineering Sciences in Medicine (APESM-ABEC) 2011. Darwin. Aug 2011.
  • Bottema, M., Bajger, M., ma, F. and Williams, S. (2011). Mathematics in medical image analysis: a focus on mammography. In Proceedings of the 6th SEAMS-GMU International Conference on Mathematics and Applications. 6th SEAMS-GMU Conference.
  • Sidik, W.A., Bottema, M. and Bajger, M. (2010). A Cross-Species Avian Human Influenza Epidemic Model: Disease Spread. In Proceedings of 54th Australian Mathematical Society Conference. Brisbane, Australia54th AustMS Conference. Brisbane. Sep 2010.
  • Ma, F., Bajger, M. and Bottema, M.J. (2008). A graph matching based automatic regional registration method for sequential mammogram analysis. In Geiger, M. L. and Karssemeijer, ed. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. SPIE. Medical Imaging 2008: Computer-Aided Diagnosis. San Diego, USA. Feb 2008.
    [10.1117/12.770322] [10.1117/12.770322] [Scopus]
  • Goel, N., Bajger, M. and Tomczak, M. (2007). Civilizations of the world, a new electronic time atlas concept. In L. Gomez Chova, D. Marti Belenguer and I. Candel Torres, ed. INTED2007 Proceedings. Valencia, SpainINTED2007: International Technology, Education and Development Conference. Valencia, Spain. Mar 2007.
  • Lee, G. and Bajger, M. (2014). Statistical temporal changes for breast cancer detection: a preliminary study. In Hiroshi Fujita, Takeshi Hara, Chisako Muramatsu, ed. Lecture Notes in Computer Science, Vol. 8539: Breast Imaging. Springer. 12th International Workshop on Breast Imaging, IWDM 2014. Gifu City, Japan. Jun 2014, pp. 635-642.
  • Bajger, M., Lee, G. and Caon, M. (2012). Full-body CT segmentation using 3D extension of two graph-based methods: a feasibility study. In M Petrou, AD Sappa & GA Triantafyllids, ed. Proceedings of the Ninth IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA 2012) Acta Press. Signal Processing, Pattern Recognition and Applications (SPPRA 2012) Crete, Greece. Jun 2012, pp. 43-50.
    [10.2316/P.2012.778-050]
  • Bottema, M., Bajger, M., Williams, S. and Ma, F. (2012). Mathematics in medical image analysis: a focus on mammography. In W Wahyuni, IE Mijayanti & D Rosadi, ed. Proceedings of the 6th SEAMS-GMU International Conference on Mathematics and Its Applications. Yogyakarta, Indonesia: Universitas Gadjah Mada. Mathematics and Its Applications in the Development of Sciences and Technology. Yogyakarta, Indonesia. Jul 2011, pp. 51-64.
  • Lee, G., Bajger, M. and Caon, M. (2012). Multi-organ segmentation of CT images using statistical region merging. In C. Hellmich, M. H. Hamza, D. Simsik, ed. Proceedings of the Ninth IASTED International Conference on Biomedical Engineering. Canada: ACTA Press. Ninth IASTED International Conference on Biomedical Engineering, BioMed 2012. Innsbruck, Austria. Feb 2012, pp. 199-206.
    [10.2316/P.2012.764-052] [Scopus]
  • Bajger, M., Ma, F., Williams, S. and Bottema, M.J. (2010). Mammographic mass detection with statistical region merging. In Proceedings: DICTA 2010. USA: IEEE. Digital Image Computing: Techniques and Applications. Sydney, NSW. Dec 2010, pp. 27-32.
  • Ma, F., Bajger, M., Williams, S. and Bottema, M.J. (2010). Improved detection of cancer in screening mammograms by temporal comparison. In Joan Marti, Arnau Oliver, Jordi Freixenet, Robert Marti, ed. Lecture Notes in Computer Science: Digital Mammography IWDM 2010. Germany: Springer-Verlag Berlin. International Workshop on Digital Mammography. Girona, Spain. Jun 2010, pp. 752-759.
  • Bajger, M., Ma, F. and Bottema, M.J. (2009). Automatic tuning of MST segmentation of mammograms for registration and mass detection algorithms. In Shi, Zhang, Bottema, Lovell, Meader, ed. DICTA 2009 Digital Image Computing Techniques and Applications. USA: IEEE Computer Society. Digital Image Computing Techniques and Applications. Melbourne, Australia. Dec 2009, pp. 400-407.
    [10.1109/DICTA.2009.72]
  • Ma, F., Bajger, M. and Bottema, M.J. (2009). Automatic mass segmentation based on adaptive pyramid and sublevel set analysis. In Shi, Zhang, Bottema, Lovell, Meader, ed. DICTA 2009 Digital Image Computing Techniques and Applications. USA: IEEE Computer Society. Digital Image Computing Techniques and Applications. Melbourne, Australia. Dec 2009, pp. 236-241.
    [10.1109/DICTA.2009.47]
  • Susukida, H., Ma, F. and Bajger, M. (2008). Automatic tuning of a graph-based image segmentation method for digital mammography applications. In 2008 5th IEEE international symposium on biomedical imaging: From nano to macro. Los Alamitos, CA: IEEE. 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI08) Paris, France. May 2008, pp. 89-92.
    [10.1109/ISBI.2008.4540939] [10.1109/ISBI.2008.4540939] [Scopus]
  • Ma, F., Bajger, M. and Bottema, M.J. (2008). Temporal analysis of mammograms based on graph matching. In Krupinski, Elizabeth, ed. Digital Mammography (Lecture Notes in Computer Science series) Berlin: Springer. 9th International Workshop on Digital Mammography. Tucson, AZ, USA. Jul 2008, pp. 158-165.
    [10.1007/978-3-540-70538-3_23] [10.1007/978-3-540-70538-3_23] [Scopus]
  • Ma, F., Bajger, M. and Bottema, M.J. (2007). Robustness of two methods for segmenting salient features in screening mammograms. In Proceedings of DICTA. Los Alamitos, USA: IEEE. 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA) Glenelg, SA. Dec 2007, pp. 112-117.
    [10.1109/DICTA.2007.4426784] [10.1109/DICTA.2007.4426784] [Scopus]
  • Bajger, M. and Omondi, A.R. (2006). Implementation of square-root and exponential functions for large FPGAs. In Chris Jesshope and Colin Egan, ed. Lecture Notes in Computer Science. Berlin, Germany: Springer. 11th Asia-Pacific Conference, ACSAC 2006. Shanghai, China. Sep 2006, pp. 6-23.
    [10.1007/11859802_3] [Scopus]
  • Ma, F., Bajger, M., Slavotinek, J.P. and Bottema, M.J. (2006). Validation of Graph Theoretic Segmentation of the Pectoral Muscle. In Astley, S. (Sue), ed. Digital Mammography - 8th International Workshop, IWDM 2006. Berlin: Springer. 8th International Workshop, IWDM 2006. Manchester, UK. Jun 2006, pp. 642-649.
    [10.1007/11783237_86] [Scopus]
  • Ma, F., Bajger, M. and Bottema, M.J. (2005). Extracting the pectoral muscle in screening mammograms using a graph pyramid. In Brian C Lovell and Anthony J Maeder, ed. Proceedings of the APRS Workshop on Digital Image Computing (WDIC) 2005. St Lucia, Queensland, Australia: University of Queensland Press. APRS Workshop on Digital Image Computing. Griffith University, Brisbane, Australia. Feb 2005.
  • Bajger, M., Ma, F. and Bottema, M.J. (2005). Minimum spanning trees and active contours for identification of the pectoral muscle in screening mamograms. In BC Lovell, AJ Maeder, T Caelli, S Ourselin, ed. Proceedings Digital Image Computing: Techniques and Applications DICTA 2005. Danvers, USA: Institute of Electrical and Electronic Engineers. Digital Image Computing Techniques and Applications 2005. Cairns, QLD, Australia. Dec 2005, pp. 47.
    [10.1109/DICTA.2005.55]
  • Caon, M., Sedlar, J., Bajger, M. and Lee, G. (2014). Computer-assisted segmentation of CT images by statistical region merging for the production of voxel models of anatomy for CT dosimetry. Australasian Physical and Engineering Sciences in Medicine, 37 pp. 393-403.
  • Bajger, M., Lee, G.N. and Caon, M. (2013). 3D segmentation for multi-organs in CT images. Electronic Letters on Computer Vision and Image Analysis, 12(2) pp. 13-27.
    [Scopus] [Web Link]
  • Bajger, M. and Omondi, A. (2008). Low-error, high-speed approximation of the sigmoid function for large FPGA Implementations. Journal of Signal Processing Systems, 52(2) pp. 137-151.
    [10.1007/s11265-007-0140-z] [10.1007/s11265-007-0140-z] [Scopus]
  • Ma, F., Bajger, M., Slavotinek, J.P. and Bottema, M.J. (2007). Two graph theory based methods for identifying the pectoral muscle in mammograms. Pattern Recognition, 40(9) pp. 2592-2602.
    [10.1016/j.patcog.2006.12.011] [10.1016/j.patcog.2006.12.011] [Scopus]
  • Bajger, M. (2004). On the composite Pexider equation modulo a subgroup. Publicationes Mathematicae-Debrecen, 64(1-2) pp. 39-61.
    [Scopus]
  • Bajger, M. (1998). On the structure of some flows on the unit circle. Aequationes mathematicae, 55 pp. 106-122.
  • Bajger, M. (1998). On generalized Cauchy and Pexider functional equations over a field. Glasnik Matematicki, 33(53) pp. 239-249.
  • Bajger, M. (1997). A generalized Pexider equation. ACTA MATHEMATICA HUNGARICA, 75 pp. 43-54.
  • Bajger, M. (1997). Iterative Pexider equation modulo a subset. Aequationes mathematicae, 53 pp. 155-161.
  • Bajger, M. (1996). On a generalized Pexider equation connected with the iteration theory. Publicationes Mathematicae-Debrecen, 48 pp. 77-88.
  • Bajger, M. (1995). Increasing solution of the translation equation. Opuscula Mathematica, 15 pp. 33-37.
  • Bajger, M. (1994). Iterative Pexider equation. Publicationes Mathematicae-Debrecen, 44 pp. 67-77.
  • Caon, M., Sedlar, J., Bajger, M. and Lee, G. (2014). Computer-assisted segmentation of CT images by statistical region merging for the production of voxel models of anatomy for CT dosimetry. Australasian Physical and Engineering Sciences in Medicine, 37 pp. 393-403.
  • Bajger, M., Lee, G.N. and Caon, M. (2013). 3D segmentation for multi-organs in CT images. Electronic Letters on Computer Vision and Image Analysis, 12(2) pp. 13-27.
    [Scopus] [Web Link]
  • Bajger, M. and Omondi, A. (2008). Low-error, high-speed approximation of the sigmoid function for large FPGA Implementations. Journal of Signal Processing Systems, 52(2) pp. 137-151.
    [10.1007/s11265-007-0140-z] [10.1007/s11265-007-0140-z] [Scopus]
  • Ma, F., Bajger, M., Slavotinek, J.P. and Bottema, M.J. (2007). Two graph theory based methods for identifying the pectoral muscle in mammograms. Pattern Recognition, 40(9) pp. 2592-2602.
    [10.1016/j.patcog.2006.12.011] [10.1016/j.patcog.2006.12.011] [Scopus]
  • Bajger, M. (2004). On the composite Pexider equation modulo a subgroup. Publicationes Mathematicae-Debrecen, 64(1-2) pp. 39-61.
    [Scopus]
  • Bajger, M. (1998). On the structure of some flows on the unit circle. Aequationes mathematicae, 55 pp. 106-122.
  • Bajger, M. (1998). On generalized Cauchy and Pexider functional equations over a field. Glasnik Matematicki, 33(53) pp. 239-249.
  • Bajger, M. (1997). A generalized Pexider equation. ACTA MATHEMATICA HUNGARICA, 75 pp. 43-54.
  • Bajger, M. (1997). Iterative Pexider equation modulo a subset. Aequationes mathematicae, 53 pp. 155-161.
  • Omondi, A.R., Rajapakse, J.C. and Bajger, M. (2006). FPGA Neurocomputers. In Amos Omondi, Jagath Rajapakse, ed. FPGA Implementations of Neural Networks. Dordrecht, The Netherlands: Springer, pp. 1-36.
    [10.1007/0-387-28487-7_1]

Professional and community engagement

Professional memberships

  • Australian Pattern Recognition Society (APRS)
  • Association for Computing Machinery (ACM)
  • Australian Mathematical Society

Journal reviewer

  • Australasian Physical and Engineering Sciences in Medicine
  • Computer Methods and Programs in Biomedicine
  • IEEE Transactions on Biomedical Engineering
  • International Journal of Computer Systems Science & Engineering
  • IEEE Transactions on Very Large Scale Integration Systems

Expertise for media contact

Interests

  • Image Analysis
  • Computer Aided Screening Mammography

Contact

Add to address book
Phone: +61 8 82013984
Email:
Location: Information, Science & Technology (226)
Postal address: GPO Box 2100, Adelaide 5001, South Australia

Further information

Current Research Projects/Publications

Students interested in research towards Honours, Master or PhD degree in the areas outlined in the projects please contact Mariusz by email/phone. Other research projects in the area of Medical Image Analysis/Pattern Recognition may also be available.

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