David Dagan Feng

University of Sydney, Sydney, New South Wales, Australia

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Publications (233)153.29 Total impact

  • Neurocomputing 11/2015; DOI:10.1016/j.neucom.2015.11.008 · 2.08 Impact Factor
  • Yangyu Fan · David Dagan Feng · Renjie He · Zhiyong Wang ·

    Electronics Letters 10/2015; DOI:10.1049/el.2015.0707 · 0.93 Impact Factor
  • Fredro Harjanto · Zhiyong Wang · Shiyang Lu · Ah Chung Tsoi · David Dagan Feng ·

  • Wei Zou · Jiajun Wang · David Dagan Feng · Erxi Fang ·
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    ABSTRACT: The analysis of fluorescence molecular tomography is important for medical diagnosis and treatment. Although the quality of reconstructed results can be improved with the increasing number of measurement data, the scale of the matrices involved in the reconstruction of fluorescence molecular tomography will also become larger, which may slow down the reconstruction process. A new method is proposed where measurement data are reduced according to the rows of the Jacobian matrix and the projection residual error. To further accelerate the reconstruction process, the global inverse problem is solved with level-by-level Schur complement decomposition. Simulation results demonstrate that the speed of the reconstruction process can be improved with the proposed algorithm. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE).
    Optical Engineering 07/2015; 54(7):073114. DOI:10.1117/1.OE.54.7.073114 · 0.95 Impact Factor
  • Yang Song · Weidong Cai · Heng Huang · Yun Zhou · Yue Wang · David Dagan Feng ·
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    ABSTRACT: In this paper, we propose a new Locality-constrained Subcluster Representation Ensemble (LSRE) model, to classify high-resolution computed tomography (HRCT) images of interstitial lung diseases (ILDs). Medical images normally exhibit large intra-class variation and inter-class ambiguity in the feature space. Modelling of feature space separation between different classes is thus problematic and this affects the classification performance. Our LSRE model tackles this issue in an ensemble classification construct. The image set is first partitioned into subclusters based on spectral clustering with approximation-based affinity matrix. Basis representations of the test image are then generated with sparse approximation from the subclusters. These basis representations are finally fused with approximation- and distribution-based weights to classify the test image. Our experimental results on a large HRCT database show good performance improvement over existing popular classifiers. Copyright © 2015 Elsevier B.V. All rights reserved.
    Medical image analysis 03/2015; 22(1). DOI:10.1016/j.media.2015.03.003 · 3.65 Impact Factor
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    ABSTRACT: Content-based image retrieval (CBIR) has been applied to a variety of medical applications, e.g., pathology research and clinical decision support, and bag-of-features (BOF) model is one of the most widely used techniques. In this study, we address the problem of vocabulary pruning to reduce the influence from the redundant and noisy visual words. The conditional probability of each word upon the hidden topics extracted using probabilistic Latent Semantic Analysis (pLSA) is firstly calculated. A ranking method is then proposed to compute the significance of the words based on the relationship between the words and topics. Experiments on the publicly available Early Lung Cancer Action Program (ELCAP) database show that the method can reduce the number of words required while improving the retrieval performance. The proposed method is applicable to general image retrieval since it is independent of the problem domain.
    Artificial Life and Computational Intelligence, 02/2015: pages 436-445;
  • Siqi Liu · Sidong Liu · Weidong Cai · Sonia Pujol · Ron Kikinis · David Dagan Feng ·
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    ABSTRACT: Feature learning with high dimensional neuroimaging features has been explored for the applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental status test scores and cerebrospinal fluid level, are essential in clinical diagnosis of neurological disorders, because they could be simple and effective for the clinicians to assess the disorder’s progression and severity. Rather than only using the low-dimensional biomarkers as inputs for decision making systems, we believe that such low-dimensional biomarkers can be used for enhancing the feature learning pipeline. In this study, we proposed a novel feature representation learning framework, Multi-Phase Feature Representation (MPFR), with low-dimensional biomarkers embedded. MPFR learns high-level neuroimaging features by extracting the associations between the low-dimensional biomarkers and the high-dimensional neuroimaging features with a deep neural network. We validated the proposed framework using the Mini-Mental-State-Examination (MMSE) scores as a low-dimensional biomarker and multi-modal neuroimaging data as the high-dimensional neuroimaging features from the ADNI baseline cohort. The proposed approach outperformed the original neural network in both binary and ternary Alzheimer’s disease classification tasks.
    Artificial Life and Computational Intelligence, 02/2015: pages 350-359;
  • Shaohui Mei · Genliang Guan · Zhiyong Wang · Shuai Wan · Mingyi He · David Dagan Feng ·
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    ABSTRACT: The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L-0 norm, instead of the relaxed constraint L-2,L-1 norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.
    Pattern Recognition 02/2015; 48(2). DOI:10.1016/j.patcog.2014.08.002 · 3.10 Impact Factor
  • Yang Song · Weidong Cai · Heng Huang · Yun Zhou · David Dagan Feng · Yue Wang · Michael J. Fulham · Mei Chen ·
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    ABSTRACT: Medical images usually exhibit large intra-class variation and inter-class ambiguity in the feature space, which could affect classification accuracy. To tackle this issue, we propose a new Large Margin Local Estimate (LMLE) classification model with sub-categorization based sparse representation. We first sub-categorize the reference sets of different classes into multiple clusters, to reduce feature variation within each subcategory compared to the entire reference set. Local estimates are generated for the test image using sparse representation with reference subcategories as the dictionaries. The similarity between the test image and each class is then computed by fusing the distances with the local estimates in a learning-based large margin aggregation construct to alleviate the problem of inter-class ambiguity. The derived similarities are finally used to determine the class label. We demonstrate that our LMLE model is generally applicable to different imaging modalities, and applied it to three tasks: interstitial lung disease (ILD) classification on high-resolution computed tomography (HRCT) images, phenotype binary classification and continuous regression on brain magnetic resonance (MR) imaging. Our experimental results show statistically significant performance improvements over existing popular classifiers.
    IEEE Transactions on Medical Imaging 01/2015; 34(6). DOI:10.1109/TMI.2015.2393954 · 3.39 Impact Factor
  • Lin Shu · Xiaoming Tao · David Dagan Feng ·
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    ABSTRACT: Resistive sensor arrays have been increasingly adopted in wearable electronic applications, which require low-complexity and low-energy circuits. However, current readout strategies for resistive sensor arrays require additional electrical components, such as transistors, diodes, multiplexers, op-amps, switches, current sources, and A/D converters, leading to a considerable increase in circuit complexity, power consumption, system instability, and crosstalk error. To address the problem, this paper proposes a new approach, which determines sensor resistance values by establishing and solving resistance matrix equations of sensor arrays. Unlike conventional approaches, it allows crosstalk currents in arrays to avoid additional components that are originally used for eliminating crosstalk currents and minimizing crosstalk error. Meanwhile, it takes advantage of on-chip resources of wearable platforms, thereby reducing redundant chips. It was implemented on a prototype of 10 × 10 textile resistive sensor array, which was taken in a sensing cushion for sitting pressure monitoring of chair bound people. Experimental results on this array platform showed the new approach achieved a satisfactory accuracy (0.61% ± 0.41%), as well as a low crosstalk error (2.77% ± 0.61%). The fabricated sensing cushion also exhibited a relatively low pressure measurement error (6.30% ± 0.75%). Compared with other approaches, the proposed approach demonstrated the lowest circuit complexity on a microcontroller based wearable platform, and a sufficient sensor capacity. It is ideal for a wide range of applications like wearable or implantable sensing, presenting a reference for the design of low-complexity and low-crosstalk error wearable systems based on resistive sensor arrays.
    IEEE Sensors Journal 01/2015; 15(1):442-452. DOI:10.1109/JSEN.2014.2333518 · 1.76 Impact Factor
  • Peng Fu · Changyang Li · Yong Xia · Zexuan Ji · Quansen Sun · Weidong Cai · David Dagan Feng ·
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    ABSTRACT: Accurate approximation of noise in hyperspectral (HS) images plays an important role in better visualization and image processing. Conventional algorithms often hypothesize the noise type to be either purely additive or of a mixed noise type for the signal-dependent (SD) noise component and the signal-independent (SI) noise component in HS images. This can result in application-driven algorithm design and limited use in different noise types. Moreover, as the highly textured HS images have abundant edges and textures, existing algorithms may fail to produce accurate noise estimation. To address these challenges, we propose a noise estimation algorithm that can adaptively estimate both purely additive noise and mixed noise in HS images with various complexities. First, homogeneous areas are automatically detected using a new region-growing-based approach, in which the similarity of two pixels is calculated by a robust spectral metric. Then, the mixed noise variance of each homogeneous region is estimated based on multiple linear regression technology. Finally, intensities of the SD and SI noise are obtained with a modified scatter plot approach. We quantitatively evaluated our algorithm on the synthetic HS data. Compared with the benchmarking and state-of-the-art algorithms, the proposed algorithm is more accurate and robust when facing images with different complexities. Experimental results with real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images further demonstrated the superiority of our algorithm.
    Applied Optics 10/2014; 53(30). DOI:10.1364/AO.53.007059 · 1.78 Impact Factor
  • Yang Song · Weidong Cail · Heng Huang · Yun Zhou · David Dagan Feng · Mei Chen ·
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    ABSTRACT: Medical images typically exhibit complex feature space distributions due to high intra-class variation and inter-class ambiguity. Monolithic classification models are often problematic. In this study, we propose a novel Large Margin Local Estimate (LMLE) method for medical image classification. In the first step, the reference images are subcategorized, and local estimates of the test image are computed based on the reference subcategories. In the second step, the local estimates are fused in a large margin model to derive the similarity level between the test image and the reference images, and the test image is classified accordingly. For evaluation, the LMLE method is applied to classify image patches of different interstitial lung disease (ILD) patterns on high-resolution computed tomography (HRCT) images. We demonstrate promising performance improvement over the state-of-the-art.
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    ABSTRACT: Neuroimaging has played an important role in non-invasive diagnosis and differentiation of neurodegenerative disorders, such as Alzheimer's disease and Mild Cognitive Impairment. Various types of features have been extracted from the neuroimaging data to characterize the disorders, and these features can be roughly divided into global and local features. Recent studies show a tendency of using the local features in disease characterization, since they are capable of identifying the subtle disease-specific patterns associated with the effects of the disease on human brain. However, problems arise if the neuroimaging database involved multiple disorders or progressive disorders, for disorders of different types or at different progressive stages might exhibit different degenerative patterns. It is difficult for the researchers to reach consensus on what brain regions could best distinguish multiple disorders or multiple progression stages. In this study we proposed a Multi-Channel pattern analysis approach to identify the most discriminative local brain metabolism features for neurodegenerative disorder characterization. We compared our method to the global methods and other pattern analysis methods based on clinical expertise or test statistics. The preliminary results suggested that the proposed Multi-Channel pattern analysis method outperformed other approaches in Alzheimer's disease characterization, and meanwhile provided important insights of underlying pathology of Alzheimer's disease and Mild Cognitive Impairment.
    Computerized Medical Imaging and Graphics 09/2014; 38(6). DOI:10.1016/j.compmedimag.2014.05.003 · 1.22 Impact Factor
  • Younhyun Jung · Jinman Kim · Michael Fulham · David Dagan Feng ·
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    ABSTRACT: Multi-modality positron emission tomography and computed tomography (PET-CT) imaging depicts biological and physiological functions (from PET) within a higher resolution anatomical reference frame (from CT). The need to efficiently assimilate the information from these co-aligned volumes simultaneously has resulted in 3D visualisation methods that depict e.g., slice of interest (SOI) from PET combined with direct volume rendering (DVR) of CT. However because DVR renders the whole volume, regions of interests (ROIs) such as tumours that are embedded within the volume may be occluded from view. Volume clipping is typically used to remove occluding structures by `cutting away' parts of the volume; this involves tedious trail-and-error tweaking of the clipping attempts until a satisfied visualisation is made, thus restricting its application. Hence, we propose a new automated opacity-driven volume clipping method for PET-CT using DVR-SOI visualisation. Our method dynamically calculates the volume clipping depth by considering the opacity information of the CT voxels in front of the PET SOI, thereby ensuring that only the relevant anatomical information from the CT is visualised while not impairing the visibility of the PET SOI. We outline the improvements of our method when compared to conventional 2D and traditional DVR-SOI visualisations.
    36th Annual Conference Proceedings of the IEEE Engineering in Medicine & Biology Society (EMBC 2014), Chicago, IL, USA; 08/2014
  • Gillian Ng · Yang Song · Weidong Cai · Yun Zhou · Sidong Liu · David Dagan Feng ·
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    ABSTRACT: With the increasing amount of image data available for cancer staging and diagnosis, it is clear that content-based image retrieval techniques are becoming more important to assist physicians in making diagnoses and tracking disease. Domain-specific feature descriptors have been previously shown to be effective in the retrieval of lung tumors. This work proposes a method to improve the rotation invariance of the hierarchical spatial descriptor, as well as presents a new binary descriptor for the retrieval of lung nodule images. The descriptors were evaluated on the ELCAP public access database, exhibiting good performance overall.
  • Genliang Guan · Zhiyong Wang · Shaohui Mei · Max Ott · Mingyi He · David Dagan Feng ·
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    ABSTRACT: While most existing video summarization approaches aim to identify important frames of a video from either a global or local perspective, we propose a top-down approach consisting of scene identification and scene summarization. For scene identification, we represent each frame with global features and utilize a scalable clustering method.We then formulate scene summarization as choosing those frames that best cover a set of local descriptors with minimal redundancy. In addition, we develop a visual word-based approach to make our approach more computationally scalable. Experimental results on two benchmark datasets demonstrate that our proposed approach clearly outperforms the state-of-the-art.
    ACM Transactions on Multimedia Computing Communications and Applications 08/2014; 11(1):1-21. DOI:10.1145/2632267 · 0.97 Impact Factor
  • Lei Bi · Jinman Kim · David Dagan Feng · Michael Fulham ·
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    ABSTRACT: Fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is the preferred image modality for lymphoma diagnosis. Sites of disease generally appear as foci of increased FDG uptake. Thresholding methods are often applied to robustly separate these regions. However, its main limitation is that it also includes sites of FDG excretion and physiological FDG uptake regions, which we define as FEPU - sites of FEPU include the bladder, renal, papillae, ureters, brain, heart and brown fat. FEPU can make image interpretation problematic. The ability to identify and label FEPU sites and separate them from abnormal regions is an important process that could improve image interpretation. We propose a new method to automatically separate and label FEPU sites from the thresholded PET images. Our method is based on the selective use of features extracted from data types comprising of PET, CT and PET-CT. Our FEPU classification of 43 clinical lymphoma patient studies revealed higher accuracy when compared to non-selective image features.
    36th Annual Conference Proceedings of the IEEE Engineering in Medicine & Biology Society (EMBC 2014), Chicago, IL, USA; 08/2014
  • Changyang Li · Xiuying Wang · Stefan Eberl · Michael Fulham · Yong Yin · David Dagan Feng ·
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    ABSTRACT: Automated and general medical image segmentation can be challenging because the foreground and the background may have complicated and overlapping density distributions in medical imaging. Conventional region-based level-set algorithms often assume piecewise constant (PC) or piecewise smooth (PS) for segments, which are implausible for general medical image segmentation. Further, low contrast and noise make identification of the boundaries between foreground and background difficult for edge-based level set algorithms. Thus, to address these problems, we suggest a supervised variational level set segmentation model to harness the statistical region energy functional with a weighted probability approximation. Our approach models the region density distributions by using the mixture-of-mixtures Gaussian model to better approximate real intensity distributions and distinguish statistical intensity differences between foreground and background. The region-based statistical model in our algorithm can intuitively provide better performance on noisy images. We constructed a weighted probability map on graphs to incorporate spatial indications from user input with a contextual constraint based on the minimization of contextual graphs energy functional. We measured the performance of our approach on 10 noisy synthetic images and 58 medical datasets with heterogeneous intensities and ill-defined boundaries and compared our technique to the Chan-Vese (CV) region-based level set model, the geodesic active contour model with distance regularization (GACD) and the random walker (RW) model. Our method consistently achieved the highest Dice similarity coefficient (DSC) when compared to the other methods.
    IEEE transactions on bio-medical engineering 07/2014; 62(1). DOI:10.1109/TBME.2014.2344660 · 2.35 Impact Factor
  • David Dagan Feng · Ewart R. Carson · J. Geoffrey Chase · Balázs Benyó ·

    Biomedical Signal Processing and Control 07/2014; 12. DOI:10.1016/j.bspc.2014.02.003 · 1.42 Impact Factor
  • Tong Zhang · Yong Xia · David Dagan Feng ·
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    ABSTRACT: The hidden Markov random field (HMRF) model has been widely used in image segmentation, as it provides a spatially constrained clustering scheme on two sets of random variables. However, in many HMRF-based segmentation approaches, both the latent class labels and statistical parameters have been estimated by deterministic techniques, which usually lead to local convergence and less accurate segmentation. In this paper, we incorporate the immune inspired clonal selection algorithm (CSA) and Markov chain Monte Carlo (MCMC) method into HMRF model estimation, and thus propose the HMRF–CSA algorithm for brain MR image segmentation. Our algorithm employs a three-step iterative process that consists of MCMC-based class labels estimation, bias field correction and CSA-based statistical parameter estimation. Since both the MCMC and CSA are global optimization techniques, the proposed algorithm has the potential to overcome the drawback of traditional HMRF-based segmentation approaches. We compared our algorithm to the state-of-the-art GA–EM algorithm, deformable cosegmentation algorithm, the segmentation routines in the widely-used statistical parametric mapping (SPM) software package and the FMRIB software library (FSL) on both simulated and clinical brain MR images. Our results show that the proposed HMRF–CSA algorithm is robust to image artifacts and can differentiate major brain structures more accurately than other three algorithms.
    Biomedical Signal Processing and Control 07/2014; 12(1):10–18. DOI:10.1016/j.bspc.2013.07.010 · 1.42 Impact Factor

Publication Stats

1k Citations
153.29 Total Impact Points


  • 2001-2015
    • University of Sydney
      • • School of Information Technologies
      • • Biomedical and Multimedia Information Technology Research Group (BMIT)
      Sydney, New South Wales, Australia
  • 2014
    • Yale University
      • Department of Diagnostic Radiology and Pediatric Diagnostic Radiology
      New Haven, Connecticut, United States
    • Budapest University of Technology and Economics
      Budapeŝto, Budapest, Hungary
  • 2009-2014
    • Shanghai Jiao Tong University
      Shanghai, Shanghai Shi, China
    • Westmead Hospital
      • Department of Medical Physics
      Sydney, New South Wales, Australia
  • 1998-2012
    • The Hong Kong Polytechnic University
      • Department of Electronic and Information Engineering
      Hong Kong, Hong Kong
  • 2008
    • Royal Prince Alfred Hospital
      • Department of Pet & Nuclear Medicine
      Camperdown, New South Wales, Australia
  • 2005
    • Heilongjiang University
      Charbin, Heilongjiang Sheng, China
  • 2000
    • Northwestern Polytechnical University
      Xi’an, Liaoning, China