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20162018
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Personal profile

Biography

Assistant Professor Abel Zhou is teaching physical principles and also radiation biology at the University of Canberra. He graduated with a Doctor of Philosophy in biological sciences from University of Canberra in Feb 2018. He has a breadth of clinical experience performing general x-ray imaging, fluoroscopy, lithotripsy kidney stones, and computer tomography at Tan Tock Seng hospital in Singapore; and processing computer tomography and magnetic resonance images for 3D stereo display and interactive surgical planning.

Abel has 4 major research programs:
1. Optimising anti-scatter grid designs for improving digital image quality and reducing ionising radiation exposure induced cancer risks
2. Improving digital image quality and reducing radiation exposures using convolutional neural networks
3. Minimising planar radiography exposures using visual grading analysis
4. Investigating radiation, molecule motion, and the second law of thermodynamics – entropy: a myth or a truth

Abel maintains strong links with Prof Rob Davidson and Dr Graeme L. White, both from University of Canberra, working on anti-scatter grid designs. He is also working with Prof Davidson on planar radiography exposure reduction using visual grading analysis.

Abel is passionate about science and research. He believes science and research are for improving human living outcomes. He is an active advocate to translate research evidence intro practice.

Student Projects Available

Applications of convolutional neural networks in x-ray imaging

 

Researchers at the University of Canberra are looking for clinical experts who maybe seeking to undertake a Master by Research or PhD. Details of the project are:

1. Introduction

Visual perceptions begin at the moment light meets the retina. Retina consists of a sheet of photoreceptors that convert light to electrical signals. These signals are sent via the optic nerve to the primary visual cortex (figure 1(a)). Signal transmissions of visual perceptions can be emulated in computer science, such as deep neural network algorithms that imitate neural processes. In computer science, neural networks which have each neuron in one layer is connected to all neurons in the next layer are fully connected (figure 1(b)) and known as multiplayer perceptrons (Gardner & Dorling, 1998). Convolutional neural networks (CNNs) are a subset of deep neural networks which can be applied to visual image analysis.

  

Figure 1(a). illustration of human visual perception (image courtesy of Brain Facts)

 

Figure 1(b). illustration of deep neuron connections

CNNs emulate animal visual processes in which an individual visual-cortex neuron reacts to stimuli only in a restricted region of the entire visual field (Fukushima, 1980; Matsugu, Mori, Mitari, & Kaneda, 2003). This restricted region is known as the receptive field of the neuron, and the receptive fields of nearby neurons partially overlap, resulting the entire visual field completely covered by the receptive fields of all neurons (Hubel & Wiesel, 1968). In applied computation and visual-imagery analysis, CNNs are a class of deep learning neural networks that are trained on existing data and then used to predict outcomes, e.g. voice recognition, facial recognition, language translation, or computer-aided diagnosis (CAD).

In medical imaging, an example application of CNNs is mammogram CAD by a number of works (Arevalo, González, Ramos-Pollán, Oliveira, & Lopez, 2016; Carneiro, Nascimento, & Bradley, 2015; Qiu et al., 2016; Shin et al., 2016; Wei, Li, & Huang, 2011).  The applications of CAD in medical imaging still require a human expert to confirm the diagnosis before it can be accepted. Human experts still have to look at the images and based on the information shown in the images, make a diagnosis.

Human experts rely on sufficient image quality to make confident diagnosis from images. X-ray image quality depends on multiple factors. The amount of radiation reaching the image detector have significant effects on the image quality. Within a certain radiation range, the higher the radiation is reaching the image detector, the better the image quality is the final image. Scatter radiation reaching the image detector is one of the main noise factors that degrade the image quality. When the relative amount of scatter radiation to primary radiation is large in the image, the image quality is often severely degraded and interferes human experts making confident diagnosis.

2. Preliminary findings

Preliminary work has been focusing on devising a CNN algorithm for x-ray image processing. The preliminary evaluation of a CNN algorithm has already been completed. The CNN algorithm is illustrated in figure 2 in which multiple features are extracted from the original radiograph and then used to enhance it to obtain an outcome of enhanced image quality. This CNN algorithm has been evaluated with both a simulated image and an abdomen radiograph.

 

Figure 2. An illustration of a convolutional neural network (CNN) processing for x-ray image quality enhancement application: multiple features are extracted and used to enhance the original image to obtain an enhanced quality  

 

In the simulation evaluation of this CNN algorithm, a PMMA phantom (30 cm x 30 cm x 30 cm) with two disc-shaped objects were used to imitate a large abdomen radiographic condition and the radiation exposures were set at one-thousandth of a typical radiation exposure. The original simulated image (3.a) fails to show the projections of the two objects. After CNN enhancement, the two projections are clearly visible (3.b). This might mean that with a perfect image receptor like the one used in the simulation, radiation exposures could be reduced by thousand folds while the image quality would still be good enough for making confident diagnosis.

 
  


(a)

 
  


(b)

Figure 3.  3.a) A simulated image of PMMA materials with dimension 30 cm by 30 cm by 30 cm with two disc-shaped objects obtained with one-thousandth of a typical radiographic exposure; 3.b) the simulated image processed with the CNN algorithm

 

In the evaluation of this CNN algorithm using an abdomen radiograph, the abdomen radiograph was taken at 75 kVp and 16 mAs without using an anti-scatter grid. The abdomen size was approximately 25 cm thick. The exposure for such abdomen sizes is about 75 kVp and 40 mAs and with an anti-scatter grid. The original radiograph is shown in figure 4.a and is not of diagnostic quality. Human experts would have low confidence in making a diagnosis from this radiograph. After enhancing it by the CNN algorithm, the enhanced outcome is shown in figure 4.b which shows significant improvement in image quality and human experts would be confidently making diagnosis from this radiograph. This means the CNN algorithm has the potential to reduce radiation exposures to patients, in this example exposure reduction by 60%, while still achieving high-quality useful radiographs.

 (a)

 (b)

Figure 4.  4.a) An abdomen radiograph of an adult with appropriately 25 cm thickness, taken with 75 kVp and 16 mAs without using an anti-scatter grid; 4.b) the abdomen radiograph after CNN algorithm enhancement

 

3. Research project

The aims of this research project are to assist human experts making confident diagnosis and at the same time reduce radiation exposures to patients hence reduce radiation-exposure-induced cancer risks for patients undergoing x-ray examinations. This project aims at two objectives: one is enhancing image quality and reducing radiation exposures to patients, the other is achieving CAD. This project may carry out in two stages. In the first stage, we will use a CNN algorithm to enhance underexposed images and provide sufficiently high image quality to assist human experts making confident diagnosis. In the second stage, the CNN algorithm will be revised and applied to make diagnosis.

3.1 Image quality enhancement using CNNs

The first stage of this research project is to evaluate the CNN algorithm for image quality enhancement. Image quality may be determined statistically or perceptually. Statistical image quality assessments are such as contrast-detail analysis (Geijer & Persliden, 2005; Perez-Ponce, Daul, Wolf, & Noel, 2011), signal-noise-ratio analysis (CNR) (Huda, Sajewicz, Ogden, & Dance, 2003), figure of merit (FOM) analysis (Fausto et al., 2017). Perceptual image quality evaluation methods are such as visual grading characteristic (VGC) analysis (Båth & Månsson, 2007; Ludewig, Richter, & Frame, 2010), visual grading analysis (VGA) (Sund, Båth, Kheddache, & Månsson, 2004; Ullman et al., 2006). The CNN algorithm is ideally evaluated using either VGC or VGA, in additional to that, contrast-detail analysis is also an added credit.

Another useful evaluation for this CNN algorithm is a cross-comparison evaluation with other image quality enhancement methods, such as the Contrast Limited Adaptive Histogram Equalization (CLAHE) (Koonsanit, Thongvigitmanee, Pongnapang, & Thajchayapong, 2017), the Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) (Sheet, Garud, Suveer, Mahadevappa, & Chatterjee, 2010), and the Histogram Equalization (HE) (Roopaei, Agaian, Shadaram, & Hurtado, 2014). A example of similar comparison publications is the study of Saenpaen, Arwatchananukul, and Aunsri (2018).

3.2 CNN algorithm for diagnosis

To apply this CNN algorithm to diagnosis may be continued after the first stage is completed or it may be concurrently performed with the image quality evaluation. Nonetheless it should be separated from the image quality evaluation. Some CNN applications in medical x-ray image are mammogram diagnosis (Arevalo et al., 2016; Carneiro et al., 2015; Qiu et al., 2016; Wei et al., 2011), lung disease diagnosis (Cheng, Feng, & Jia, 2018), vessel extraction in x-ray angiograms (Nasr-Esfahani et al., 2016).

4. Research candidates

The research activities proposed above are suitable for both research master and PhD candidates.

The research candidates are responsible for image quality evaluation activities by VGC or VGA as well as other image quality evaluation methods. The candidates under the supervision will be expected to perform a variety of research activities, which include literature review, ethic applications, recruitment of participants (if needed), data analysis and dissemination in peer reviewed journals, as well as other research activities within the overall scope of a research project.

PhD candidates who are interested in expanding applications of this CNN algorithm are also welcome to join the team.

 

Contacts:

Principle Supervisor:

Dr Abel Zhou, PhD.  Abel.Zhou@canberra.edu.au

Co-supervisors:

Prof Rob Davidson, PhD.  Rob.Davidson@canberra.edu.au

Prof Nick Brown, PhD.   Nick.Brown@canberra.edu.au

 

References

Arevalo, J., González, F. A., Ramos-Pollán, R., Oliveira, J. L., & Lopez, M. A. G. (2016). Representation learning for mammography mass lesion classification with convolutional neural networks. Computer methods and programs in biomedicine, 127, 248-257.

Båth, M., & Månsson, L. G. (2007). Visual grading characteristics (VGC) analysis: a non-parametric rank-invariant statistical method for image quality evaluation. The British Journal of Radiology, 80(951), 169-176. doi:10.1259/bjr/35012658

Carneiro, G., Nascimento, J., & Bradley, A. P. (2015). Unregistered multiview mammogram analysis with pre-trained deep learning models. Paper presented at the International Conference on Medical Image Computing and Computer-Assisted Intervention.

Cheng, Y., Feng, J., & Jia, K. (2018, 12-15 Nov. 2018). A Lung Disease Classification Based on Feature Fusion Convolutional Neural Network with X-ray Image Enhancement. Paper presented at the 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

Fausto, A. M. F., Lopes, M. C., de Sousa, M. C., Furquim, T. A. C., Mol, A. W., & Velasco, F. G. (2017). Optimization of Image Quality and Dose in Digital Mammography. Journal of digital imaging, 30(2), 185-196. doi:10.1007/s10278-016-9928-3

Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics, 36(4), 193-202.

Gardner, M. W., & Dorling, S. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment, 32(14-15), 2627-2636.

Geijer, H., & Persliden, J. (2005). Varied tube potential with constant effective dose at lumbar spine radiography using a flat-panel digital detector. Radiation Protection Dosimetry, 114(1-3), 240-245. doi:10.1093/rpd/nch509

Hubel, D. H., & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. The Journal of physiology, 195(1), 215-243.

Huda, W., Sajewicz, A. M., Ogden, K. M., & Dance, D. R. (2003). Experimental investigation of the dose and image quality characteristics of a digital mammography imaging system. medical physics, 30(3), 442-448.

Koonsanit, K., Thongvigitmanee, S., Pongnapang, N., & Thajchayapong, P. (2017, 31 Aug.-2 Sept. 2017). Image enhancement on digital x-ray images using N-CLAHE. Paper presented at the 2017 10th Biomedical Engineering International Conference (BMEiCON).

Ludewig, E., Richter, A., & Frame, M. (2010). Diagnostic imaging–evaluating image quality using visual grading characteristic (VGC) analysis. Veterinary Research Communications, 34(5), 473-479.

Matsugu, M., Mori, K., Mitari, Y., & Kaneda, Y. (2003). Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Networks, 16(5-6), 555-559.

Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Ward, K., Jafari, M. H., . . . Najarian, K. (2016, 16-20 Aug. 2016). Vessel extraction in X-ray angiograms using deep learning. Paper presented at the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Perez-Ponce, H., Daul, C., Wolf, D., & Noel, A. (2011). Computation of realistic virtual phantom images for an objective lesion detectability assessment in digital mammography. Medical Engineering & Physics, 33(10), 1276-1286. doi:http://dx.doi.org/10.1016/j.medengphy.2011.06.004

Qiu, Y., Yan, S., Tan, M., Cheng, S., Liu, H., & Zheng, B. (2016). Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study. Paper presented at the Medical Imaging 2016: Computer-Aided Diagnosis.

Roopaei, M., Agaian, S., Shadaram, M., & Hurtado, F. (2014). Cross-entropy histogram equalization. Paper presented at the 2014 IEEE international conference on systems, man, and cybernetics (SMC).

Saenpaen, J., Arwatchananukul, S., & Aunsri, N. (2018, 18-21 July 2018). A Comparison of Image Enhancement Methods for Lumbar Spine X-ray Image. Paper presented at the 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).

Sheet, D., Garud, H., Suveer, A., Mahadevappa, M., & Chatterjee, J. (2010). Brightness preserving dynamic fuzzy histogram equalization. IEEE Transactions on Consumer Electronics, 56(4), 2475-2480.

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., . . . Summers, R. M. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285-1298.

Sund, P., Båth, M., Kheddache, S., & Månsson, L. G. (2004). Comparison of visual grading analysis and determination of detective quantum efficiency for evaluating system performance in digital chest radiography. European Radiology, 14(1), 48-58.

Ullman, G., Sandborg, M., Tingberg, A., Dance, D. R., Hunt, R., & Carlsson, G. A. (2006). Comparison of clinical and physical measures of image quality in chest and pelvis computed radiography at different tube voltages. medical physics, 33(11), 4169-4175.

Wei, C.-H., Li, Y., & Huang, P. J. (2011). Mammogram retrieval through machine learning within BI-RADS standards. Journal of biomedical informatics, 44(4), 607-614.

 

Education/Academic qualification

PhD

31 Mar 20158 Jan 2018

Bachelor

Feb 2000Feb 2002

Charles Sturt University

Jun 2014Mar 2015

Master, Charles Sturt University

Feb 2013Jun 2014

External positions

Inspector, Qingyuan Shengli Copper material Co. Ltd.

May 2008Dec 2011

clinical application specialist, Volume Interactions Pte. Ltd.

Dec 2005Feb 2008

Senior Radiographer, National Neuroscience Institute of Singapore

Jul 2005Dec 2005

Radiographer, Tan Tock Seng Hospital

Jun 1999Jul 2005

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Research Output 2016 2018

X-Rays
Radiation
Monte Carlo Method
Photons
1 Citation (Scopus)

A new solution for radiation transmission in anti-scatter grids

ZHOU, A., WHITE, G., DAVIDSON, R. & Yin, Y., 21 Sep 2016, In : Biomedical Physics and Engineering Express. 2, 5, p. 1-16 16 p.

Research output: Contribution to journalArticle

Wave transmission
grids
Radiation
radiation
strip