Dr Shakes Chandra
Dr

Shakes Chandra

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Phone: 
+61 7 336 58359

Background

Shakes an imaging expert that leads a strong deep learning, artificial intelligence (AI) focused research team interested in medical image analysis and signal/image processing applied to many areas of science and medicine. He received his Ph.D in Theoretical Physics from Monash University, Melbourne and has been involved in applying machine learning in medical imaging for over a decade.

Shakes’ past work has involved developing shape model-based algorithms for knee, hip and shoulder joint segmentation that is being developed and deployed as a product on the Siemens syngo.via platform. More recent work involves deep learning based algorithms for semantic segmentation and manifold learning of imaging data. Broadly, he is interested in understanding and developing the mathematical basis of imaging, image analysis algorithms and physical systems. He has developed algorithms that utilise exotic mathematical structures such as fractals, turbulence, group theoretic concepts and number theory in the image processing approaches that he has developed.

He is currently a Senior Lecturer and leads a team of 20+ researchers working image analysis and AI research across healthcare and medicine. He currently teaches the computer science courses Theory of Computation and Pattern Recognition and Analysis.

Availability

Dr Shakes Chandra is:
Available for supervision

Fields of research

Artificial intelligence Biomedical engineering Biomedical imaging Combinatorics and discrete mathematics (excl. physical combinatorics) Computational imaging Computer vision and multimedia computation Deep learning Engineering Image processing Information and Computing Sciences Machine learning Mathematical Sciences Pure mathematics Theory of computation

Qualifications

  • Doctor of Philosophy, Monash University

Research interests

  • Magnetic Resonance Imaging

    Making MRI faster and more affordable through better image reconstruction, processing and analysis.

  • Image Processing

    Image reconstruction, segmentation and registration.

  • Deep learning

    Dimensionality reduction, machine learning and Artificial Intelligence

  • Fractals and Chaos

    Applying fractals and chaos to image processing and computer science.

  • Number Theory

    Applying number theory to image processing and computer science.

  • Medical Image Analysis

    Medical image segmentation and shape analysis

Research impacts

Times Higher Education - A joint effort: machine learning’s search for the first MRI-based biomarker of osteoarthritis

UQNews - Making magnetic resonance imaging (MRI) faster using fractals and turbulence.

Search Professor Shakes Chandra’s works on UQ eSpace

106 works between 2006 and 2025
All (106) Journal Article (52) Other Outputs (5) Conference Publication (49)

81 - 100 of 106 works

2014

Journal Article

Exact image representation via a number-theoretic Radon transform

Chandra, Shekhar S. and Svalbe, Imants (2014). Exact image representation via a number-theoretic Radon transform. IET Computer Vision, 8 (4), 338-346. doi: 10.1049/iet-cvi.2013.0101

Exact image representation via a number-theoretic Radon transform

2014

Journal Article

Robust digital image reconstruction via the discrete Fourier slice theorem

Chandra, Shekhar S., Normand, Nicolas, Kingston, Andrew, Guedon, Jeanpierre and Svalbe, Imants (2014). Robust digital image reconstruction via the discrete Fourier slice theorem. IEEE Signal Processing Letters, 21 (6) 6777574, 682-686. doi: 10.1109/LSP.2014.2313341

Robust digital image reconstruction via the discrete Fourier slice theorem

2014

Journal Article

Focused shape models for hip joint segmentation in 3D magnetic resonance images

Chandra, Shekhar S., Xia, Ying, Engstrom, Craig, Crozier, Stuart, Schwarz, Raphael and Fripp, Jurgen (2014). Focused shape models for hip joint segmentation in 3D magnetic resonance images. Medical Image Analysis, 18 (3), 567-578. doi: 10.1016/j.media.2014.02.002

Focused shape models for hip joint segmentation in 3D magnetic resonance images

2014

Conference Publication

Fast cine-magnetic resonance imaging point tracking for prostate cancer radiation therapy planning

Dowling, Jason, Dang, K., Fox, Chris D., Chandra, S., Gill, Suki, Kron, T., D Pham, D. and Foroudi, F. (2014). Fast cine-magnetic resonance imaging point tracking for prostate cancer radiation therapy planning. ICCR 2013: XVII International Conference on the Use of Computers in Radiation Therapy, Melbourme, VIC, Australia, 6–9 May 2013. Bristol, United Kingdom: Institute of Physics Publishing. doi: 10.1088/1742-6596/489/1/012027

Fast cine-magnetic resonance imaging point tracking for prostate cancer radiation therapy planning

2014

Conference Publication

Automatic atlas based electron density and structure contouring for MRI-based prostate radiation therapy on the cloud

Dowling, J. A., Burdett, N., Greer, P. B., Sun, J., Parker, J., Pichler, P., Stanwell, P., Chandra, S., Rivest-Henault, D., Ghose, S., Salvado, O. and Fripp, J. (2014). Automatic atlas based electron density and structure contouring for MRI-based prostate radiation therapy on the cloud. XVII International Conference on the Use of Computers in Radiation Therapy, Melbourne, Australia, 6–9 May 2013. Bristol, United Kingdom: Institute of Physics Publishing. doi: 10.1088/1742-6596/489/1/012048

Automatic atlas based electron density and structure contouring for MRI-based prostate radiation therapy on the cloud

2014

Conference Publication

Automatic Bone Segmentation for Shoulder {MRI} using Statistical Shape Models

Yang, Zhengyi, Fripp, Jurgen, Engstrom, Craig, Chandra, Shekhar, Xia, Ying, Paproki, Anthony, Strudwick, Mark, Neubert, Ales and Crozier, Stuart (2014). Automatic Bone Segmentation for Shoulder {MRI} using Statistical Shape Models. 2014 – Joint Annual Meeting ISMRM-ESMRMB, 22nd Scientific Meeting and Exhibition, Milan, Italy, 10-16 May 2014. Berkeley, CA United States: International Society for Magnetic Resonance in Medicine.

Automatic Bone Segmentation for Shoulder {MRI} using Statistical Shape Models

2014

Journal Article

Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images: data from the Osteoarthritis Initiative

Paproki, A., Engstrom, C., Chandra, S. S., Neubert, A., Fripp, J. and Crozier, S. (2014). Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images: data from the Osteoarthritis Initiative. Osteoarthritis and Cartilage, 22 (9), 1259-1270. doi: 10.1016/j.joca.2014.06.029

Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images: data from the Osteoarthritis Initiative

2013

Journal Article

Endorectal balloons in the post prostatectomy setting: Do gains in stability lead to more predictable dosimetry?

Jameson, Michael G., De Leon, Jeremiah, Windsor, Apsara A., Cloak, Kirrily, Keats, Sarah, Dowling, Jason A, Chandra, Shekhar S., Vial, Philip, Sidhom, Mark, Holloway, Lois and Metcalfe, Peter (2013). Endorectal balloons in the post prostatectomy setting: Do gains in stability lead to more predictable dosimetry?. Radiotherapy and Oncology, 109 (3), 493-497. doi: 10.1016/j.radonc.2013.08.024

Endorectal balloons in the post prostatectomy setting: Do gains in stability lead to more predictable dosimetry?

2013

Journal Article

Automated bone segmentation from large field of view 3D MR images of the hip joint

Xia, Ying, Fripp, Jurgen, Chandra, Shekhar S., Schwarz, Raphael, Engstrom, Craig and Crozier, Stuart (2013). Automated bone segmentation from large field of view 3D MR images of the hip joint. Physics in Medicine and Biology, 58 (20), 7375-7390. doi: 10.1088/0031-9155/58/20/7375

Automated bone segmentation from large field of view 3D MR images of the hip joint

2013

Conference Publication

Direct Inversion of Mojette Projections

Svalbe, Imants, Kingston, Andrew, Guedon, Jeanpierre, Normand, Nicolas and Chandra, Shekhar S. (2013). Direct Inversion of Mojette Projections. 20th IEEE International Conference on Image Processing, ICIP 2013, Melbourne , Australia, 15 - 18 September 2013. Piscataway, NJ United States: IEEE. doi: 10.1109/ICIP.2013.6738214

Direct Inversion of Mojette Projections

2012

Journal Article

Patient specific prostate segmentation in 3-D magnetic resonance images

Chandra, Shekhar S., Dowling, Jason A., Shen, Kai-Kai, Raniga, Parnesh, Pluim, Josien P. W., Greer, Peter B., Salvado, Olivier and Fripp, Jurgen (2012). Patient specific prostate segmentation in 3-D magnetic resonance images. IEEE Transactions On Medical Imaging, 31 (10) 6257497, 1955-1964. doi: 10.1109/TMI.2012.2211377

Patient specific prostate segmentation in 3-D magnetic resonance images

2012

Journal Article

Recovering missing slices of the discrete fourier transform using ghosts

Chandra, Shekhar S., Svalbe, Imants D., Guedon, Jeanpierre, Kingston, Andrew M. and Normand, Nicolas (2012). Recovering missing slices of the discrete fourier transform using ghosts. IEEE Transactions on Image Processing, 21 (10) 6226457, 4431-4441. doi: 10.1109/TIP.2012.2206033

Recovering missing slices of the discrete fourier transform using ghosts

2012

Conference Publication

Automated bone segmentation and bone-cartilage interface extraction from MR images of the hip

Xia, Ying, Chandra, Shakes, Salvado, Oliver, Fripp, Jurgen, Schwartz, Raphael, Lauer, Lars, Engstrom, Craig M. and Crozier, Stuart (2012). Automated bone segmentation and bone-cartilage interface extraction from MR images of the hip. International Society for Magnetic Resonance in Medicine, Melbourme, VIC, Australia, 5-11 May 2012.

Automated bone segmentation and bone-cartilage interface extraction from MR images of the hip

2012

Conference Publication

Unilateral hip joint segmentation with shape priors learned from missing data

Chandra, Shekhar, Xia, Yinq, Engstrom, Craig, Schwarz, Raphael, Lauer, Lars, Crozier, Stuart, Salvado, Olivier and Fripp, Jurgen (2012). Unilateral hip joint segmentation with shape priors learned from missing data. 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain, 2-5 March 2012. Piscataway, NJ, United States: IEEE. doi: 10.1109/ISBI.2012.6235909

Unilateral hip joint segmentation with shape priors learned from missing data

2012

Conference Publication

Morphology-based interslice interpolation on manual segmentations of joint bones and muscles in MRI

Yang, Zhengyi, Crozier, Stuart, Engstrom, Craig, Xia, Ying, Neubert, Ales, Brancato, Tania, Schwarz, Raphael, Lauer, Lars, Fripp, Jurgen, Chandra, Shekhar and Salvado, Olivier (2012). Morphology-based interslice interpolation on manual segmentations of joint bones and muscles in MRI. 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), Fremantle, WA, Australia, 3-5 December 2012. Piscataway, NJ, United States: IEEE. doi: 10.1109/DICTA.2012.6411678

Morphology-based interslice interpolation on manual segmentations of joint bones and muscles in MRI

2011

Conference Publication

Fast automatic multi-atlas segmentation of the prostate from 3D MR images

Dowling, Jason A., Fripp, Jurgen, Chandra, Shekhar, Pluim, Josien P. W., Lambert, Jonathan, Parker, Joel, Denham, James, Greer, Peter B. and Salvado, Olivier (2011). Fast automatic multi-atlas segmentation of the prostate from 3D MR images. 14th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), Toronto, Canada, 18-22 September 2011. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-23944-1_2

Fast automatic multi-atlas segmentation of the prostate from 3D MR images

2011

Conference Publication

Automatic segmentation of the prostate in 3D magnetic resonance images using case specific deformable models

Chandra, Shekhar, Dowling, Jason, Shen, Kaikai, Pluim, Josien, Greer, Peter, Salvado, Olivier and Fripp, Jurgen (2011). Automatic segmentation of the prostate in 3D magnetic resonance images using case specific deformable models. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011, Noosa Heads, Qld., Australia, 6-8 December 2011. Piscataway, NJ, United States: I E E E. doi: 10.1109/DICTA.2011.10

Automatic segmentation of the prostate in 3D magnetic resonance images using case specific deformable models

2011

Conference Publication

Automated MR hip bone segmentation

Xia, Ying, Chandra, Shakes, Salvado, Olivier, Fripp, Jurgen, Schwarz, Raphael, Lauer, Lars, Engstrom, Craig and Crozier, Stuart (2011). Automated MR hip bone segmentation. International Conference on Digital Image Computing Techniques and Applications (DICTA), Noosa, QLD, Australia, 6-8 December 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/DICTA.2011.13

Automated MR hip bone segmentation

2011

Conference Publication

Growth of Discrete Projection Ghosts Created by Iteration

Svalbe, Imants and Chandra, Shekhar (2011). Growth of Discrete Projection Ghosts Created by Iteration. 16th International Conference on Discrete Geometry for Computer Imagery, Nancy France, 6 - 8 April 2011. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-19867-0_34

Growth of Discrete Projection Ghosts Created by Iteration

2010

Other Outputs

Circulant theory of the Radon transform

Chandra, Shekhar Suresh (2010). Circulant theory of the Radon transform. PhD Thesis, Faculty of Science, School of Physics, Monash University.

Circulant theory of the Radon transform

Current funding

  • 2026 - 2029
    Next generation magnetic resonance imaging through vision
    ARC Future Fellowships
    Open grant
  • 2025 - 2027
    Cost effective and portable low-field musculoskeletal MRI for high performance sport
    Australia's Economic Accelerator Innovate Grants
    Open grant
  • 2020 - 2026
    PREDICT-TBI - PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury: the value of Magnetic Resonance Imaging
    NHMRC MRFF Traumatic Brain Injury Mission
    Open grant

Past funding

  • 2022 - 2025
    Advancing the visualisation and quantification of nephrons with MRI
    ARC Discovery Projects
    Open grant
  • 2022 - 2025
    Robust, valid and interpretable deep learning for quantitative imaging
    ARC Linkage Projects
    Open grant
  • 2021 - 2024
    ChondralHealth Productization: Automated Musculoskeletal MR Image Analysis Algorithms
    Siemens Healthcare Pty Ltd
    Open grant
  • 2021 - 2024
    Osteoarthritis Compass: Personalized prediction of disease onset and progression. (NHMRC Ideas Grant administered by Griffith University)
    Griffith University
    Open grant
  • 2018 - 2022
    MR Hip Intervention and Planning System to enhance clinical and surgical outcomes
    NHMRC Development Grant
    Open grant

Availability

Dr Shakes Chandra is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Available projects

  • Next generation magnetic resonance imaging MRI through vision

    Summary: Magnetic resonance imaging (MRI) is crucial for diagnosing diseases within the human body. In this project, we develop new AI methods that leverage human visual perception to make MRI faster and more affordable.

    Technologies such as magnetic resonance imaging (MRI) are essential in healthcare for non-invasively seeing inside the human body for disease diagnosis and assessment. However, imaging cost for MRI is so prohibitive that it is seldom used unless there is no other option despite its effectiveness. The cost is largely because MRI is a slow imaging modality compared to other options that do not provide as much information and soft tissue contrast needed to detect diseases such as cancer. Although some progress has been made to improve acquisition speed, all current methods do not make any allowances for the way that human experts read and understand regions of interest. A reduction in scan time will make MRI cheaper and therefore allow the technology to be more readily utilised in the future.

    This project aims to create new artificial intelligence (AI) models and unify them with MRI acquisition directly in its measurement domain, helping us explain such models and create acquisitions more akin to human vision that only acquires the areas an operator needs, thereby reducing scan times.

Supervision history

Current supervision

Completed supervision

Enquiries

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