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

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

61 - 80 of 106 works

2020

Journal Article

Simultaneous super-resolution and contrast synthesis of routine clinical magnetic resonance images of the knee for improving automatic segmentation of joint cartilage: data from the Osteoarthritis Initiative

Neubert, Aleš, Bourgeat, Pierrick, Wood, Jason, Engstrom, Craig, Chandra, Shekhar S., Crozier, Stuart and Fripp, Jurgen (2020). Simultaneous super-resolution and contrast synthesis of routine clinical magnetic resonance images of the knee for improving automatic segmentation of joint cartilage: data from the Osteoarthritis Initiative. Medical Physics, 47 (10) mp.14421, 4939-4948. doi: 10.1002/mp.14421

Simultaneous super-resolution and contrast synthesis of routine clinical magnetic resonance images of the knee for improving automatic segmentation of joint cartilage: data from the Osteoarthritis Initiative

2020

Conference Publication

Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN

Min, Hang, Wilson, Devin, Huang, Yinhuang, Liu, Siyu, Crozier, Stuart, Bradley, Andrew P. and Chandra, Shekhar S. (2020). Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN. 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, United States, 3-7 April 2020. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/isbi45749.2020.9098732

Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN

2020

Conference Publication

Fast high dynamic range MRI by Contrast Enhancement Networks

Marques, Matthew, Engstrom, Craig, Fripp, Jurgen, Crozier, Stuart and Chandra, Shekhar S. (2020). Fast high dynamic range MRI by Contrast Enhancement Networks. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, United States, 3-7 April 2020. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/isbi45749.2020.9098373

Fast high dynamic range MRI by Contrast Enhancement Networks

2019

Journal Article

Multi-scale sifting for mammographic mass detection and segmentation

Min, Hang, Chandra, Shekhar S, Crozier, Stuart and Bradley, Andrew P (2019). Multi-scale sifting for mammographic mass detection and segmentation. Biomedical Physics and Engineering Express, 5 (2) 025022, 025022. doi: 10.1088/2057-1976/aafc07

Multi-scale sifting for mammographic mass detection and segmentation

2018

Journal Article

Local contrast-enhanced MR images via high dynamic range processing

Chandra, Shekhar S., Engstrom, Craig, Fripp, Jurgen, Neubert, Ales, Jin, Jin, Walker, Duncan, Salvado, Olivier, Ho, Charles and Crozier, Stuart (2018). Local contrast-enhanced MR images via high dynamic range processing. Magnetic Resonance in Medicine, 80 (3), 1206-1218. doi: 10.1002/mrm.27109

Local contrast-enhanced MR images via high dynamic range processing

2018

Journal Article

Chaotic Sensing

Chandra, Shekhar S., Ruben, Gary, Jin, Jin, Li, Mingyan, Kingston, Andrew, Svalbe, Imants and Crozier, Stuart (2018). Chaotic Sensing. IEEE Transactions on Image Processing, 27 (12) 8432445, 1-1. doi: 10.1109/TIP.2018.2864918

Chaotic Sensing

2018

Journal Article

A lightweight rapid application development framework for biomedical image analysis

Chandra, Shekhar S., Dowling, Jason A., Engstrom, Craig, Xia, Ying, Paproki, Anthony, Neubert, Aleš, Rivest-Hénault, David, Salvado, Olivier, Crozier, Stuart and Fripp, Jurgen (2018). A lightweight rapid application development framework for biomedical image analysis. Computer Methods and Programs in Biomedicine, 164, 193-205. doi: 10.1016/j.cmpb.2018.07.011

A lightweight rapid application development framework for biomedical image analysis

2018

Conference Publication

SPIFFY: a simpler image viewer for medical imaging

Sun, Jiayu and Chandra, Shekhar S. (2018). SPIFFY: a simpler image viewer for medical imaging. 4th Information Technology and Mechatronics Engineering Conference (ITOEC2018), Chongqing, China, 14-16 December 2018. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/ITOEC.2018.8740656

SPIFFY: a simpler image viewer for medical imaging

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

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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