Dr Shakes Chandra
Dr

Shakes Chandra

Email: 
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

98 works between 2006 and 2025
All (98) Journal Article (49) Conference Publication (46) Other Outputs (3)

61 - 80 of 98 works

2017

Conference Publication

Multi-scale mass segmentation for mammograms via cascaded random forests

Min, Hang, Chandra, Shekhar S., Dhungel, Neeraj, Crozier, Stuart and Bradley, Andrew P. (2017). Multi-scale mass segmentation for mammograms via cascaded random forests. 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, VIC, Australia, 18 - 21 April 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ISBI.2017.7950481

Multi-scale mass segmentation for mammograms via cascaded random forests

2016

Journal Article

Fast automated segmentation of multiple objects via spatially weighted shape learning

Chandra, Shekhar S., Dowling, Jason A., Greer, Peter B., Martin, Jarad, Wratten, Chris, Pichler, Peter, Fripp, Jurgen and Crozier, Stuart (2016). Fast automated segmentation of multiple objects via spatially weighted shape learning. Physics in Medicine and Biology, 61 (22), 8070-8084. doi: 10.1088/0031-9155/61/22/8070

Fast automated segmentation of multiple objects via spatially weighted shape learning

2016

Journal Article

Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

Neubert, A., Yang, Z., Engstrom, C., Xia, Y., Strudwick, M. W., Chandra, S. S., Fripp, J. and Crozier, S. (2016). Automatic segmentation of the glenohumeral cartilages from magnetic resonance images. Medical Physics, 43 (10), 5370-5379. doi: 10.1118/1.4961011

Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

2016

Conference Publication

Incremental shape learning of 3D surfaces of the knee, data from the osteoarthritis initiative

Neubert, Ales, Naser, Ibrahim, Paproki, Anthony, Engstrom, Craig, Fripp, Jurgen, Crozier, Stuart and Chandra, Shekhar S. (2016). Incremental shape learning of 3D surfaces of the knee, data from the osteoarthritis initiative. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, 13-16 April, 2016. Piscataway, United States: IEEE Operations Center. doi: 10.1109/ISBI.2016.7493406

Incremental shape learning of 3D surfaces of the knee, data from the osteoarthritis initiative

2016

Conference Publication

Automated intervertebral disc segmentation using probabilistic shape estimation and active shape models

Neubert, Aleš, Fripp, Jurgen, Chandra, Shekhar S., Engstrom, Craig and Crozier, Stuart (2016). Automated intervertebral disc segmentation using probabilistic shape estimation and active shape models. Third International Workshop and Challenge, CSI 2015, Munich, Germany, 5 October 2015. Switzerland: Springer. doi: 10.1007/978-3-319-41827-8_15

Automated intervertebral disc segmentation using probabilistic shape estimation and active shape models

2016

Conference Publication

Finite radial reconstruction for magnetic resonance imaging: a theoretical study

Chandra, Shekhar S., Archchige, Ramitha, Ruben, Gary, Jin, Jin, Li, Mingyan, Kingston, Andrew M., Svalbe, Imants and Crozier, Stuart (2016). Finite radial reconstruction for magnetic resonance imaging: a theoretical study. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, Gold Coast, QLD, Australia, 30 November-2 December 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA.2016.7797043

Finite radial reconstruction for magnetic resonance imaging: a theoretical study

2015

Journal Article

Automatic substitute computed tomography generation and contouring for magnetic resonance imaging (MRI)-alone external beam radiation therapy from standard MRI sequences

Dowling, Jason A., Sun, Jidi, Pichler, Peter, Rivest-Hénault, David, Ghose, Soumya, Richardson, Haylea, Wratten, Chris, Martin, Jarad, Arm, Jameen, Best, Leah, Chandra, Shekhar S., Fripp, Jurgen, Menk, Frederick W. and Greer, Peter B. (2015). Automatic substitute computed tomography generation and contouring for magnetic resonance imaging (MRI)-alone external beam radiation therapy from standard MRI sequences. International Journal of Radiation Oncology Biology Physics, 93 (5), 1144-1153. doi: 10.1016/j.ijrobp.2015.08.045

Automatic substitute computed tomography generation and contouring for magnetic resonance imaging (MRI)-alone external beam radiation therapy from standard MRI sequences

2015

Journal Article

Automated 3D quantitative assessment and measurement of alpha angles from the femoral head-neck junction using MR imaging

Xia, Ying, Fripp, Jurgen, Chandra, Shekhar S., Walker, Duncan, Crozier, Stuart and Engstrom, Craig M. (2015). Automated 3D quantitative assessment and measurement of alpha angles from the femoral head-neck junction using MR imaging. Physics in Medicine and Biology, 60 (19), 7601-7616. doi: 10.1088/0031-9155/60/19/7601

Automated 3D quantitative assessment and measurement of alpha angles from the femoral head-neck junction using MR imaging

2015

Journal Article

Automated analysis of hip joint cartilage combining MR T2 and three-dimensional fast-spin-echo images

Chandra, Shekhar S., Surowiec, Rachel, Ho, Charles, Xia, Ying, Engstrom, Craig M., Crozier, Stuart and Fripp, Jurgen (2015). Automated analysis of hip joint cartilage combining MR T2 and three-dimensional fast-spin-echo images. Magnetic Resonance in Medicine, 75 (1), 403-413. doi: 10.1002/mrm.25598

Automated analysis of hip joint cartilage combining MR T2 and three-dimensional fast-spin-echo images

2015

Journal Article

Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images

Yang, Zhengyi, Fripp, Jurgen, Chandra, Shekhar S., Neubert, Ales, Xia, Ying, Strudwick, Mark, Paproki, Anthony, Engstrom, Craig and Crozier, Stuart (2015). Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images. Physics in Medicine and Biology, 60 (4), 1441-1459. doi: 10.1088/0031-9155/60/4/1441

Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images

2015

Conference Publication

Shape reconstruction using EM-ICP mesh registration and robust statistical shape models

Neubert, Ales, Chandra, Shekhar S., Engstrom, Craig, Fripp, Jurgen and Crozier, Stuart (2015). Shape reconstruction using EM-ICP mesh registration and robust statistical shape models. Symposium on Statistical Shape Models Applications, Delemont, Switzerland, 30 September - 2 October, 2015.

Shape reconstruction using EM-ICP mesh registration and robust statistical shape models

2014

Journal Article

Automatic hip cartilage segmentation from 3D MR images using arc-weighted graph searching

Xia, Ying, Chandra, Shekhar S., Engstrom, Craig M., Strudwick, Mark W., Crozier, Stuart and Fripp, Jurgen (2014). Automatic hip cartilage segmentation from 3D MR images using arc-weighted graph searching. Physics In Medicine And Biology, 59 (23), 7245-7266. doi: 10.1088/0031-9155/59/23/7245

Automatic hip cartilage segmentation from 3D MR images using arc-weighted graph searching

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

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

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

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?

Current funding

  • 2022 - 2025
    Advancing the visualisation and quantification of nephrons with MRI
    ARC Discovery Projects
    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
    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

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Available projects

  • Machine learning applied to 3D magnetic resonance images

    Magnetic resonance (MR) imaging has become an important non-invasive radiological modality for various clinical applications, such as cartilage assessment for Osteoarthritis and treatment planning for prostate cancer. MR images in 3D, while providing a wealth of anatomical information, including bones and soft tissue, are difficult to analyse due to the presence of a large number of complex structures. Thus, extracting meaningful clinical information without human interaction is a challenging task. Developing such automatic methods are important in order to reduce human errors and the time taken by clinicians in completing mundane tasks, such as marking or delineating 3D images by hand, from hours to just a few minutes by utilising computers.

    In this project, the student will develop novel algorithms to solve segmentation and detection problems for MR imaging that could possibly be deployed to MRI scanners and may eventually used for diagnostic purposes. The project will involve applying computer vision and machine learning techniques (including deep learning) to MR image processing and analysis.

Supervision history

Current supervision

Completed supervision

Enquiries

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