[Show abstract][Hide abstract] ABSTRACT: Retinal imaging can facilitate the measurement and quantification of subtle variations and abnormalities in retinal vasculature. Retinal vascular imaging may thus offer potential as a noninvasive research tool to probe the role and pathophysiology of the microvasculature, and as a cardiovascular risk prediction tool. In order to perform this, an accurate method must be provided that is statistically sound and repeatable. This paper presents the methodology of such a system that assists physicians in measuring vessel caliber (i.e., diameters or width) from digitized fundus photographs. The system involves texture and edge information to measure and quantify vessel caliber. The graphical user interfaces are developed to allow retinal image graders to select individual vessel area that automatically returns the vessel calibers for noisy images. The accuracy of the method is validated using the measured caliber from graders and an existing method. The system provides very high accuracy vessel caliber measurement which is also reproducible with high consistency.
Computers in biology and medicine 01/2014; 44:1–9. · 1.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: It is well known that processing big graph data can be costly on Cloud. Processing big graph data introduces complex and multiple iterations that raise challenges such as parallel memory bottlenecks, deadlocks, and inefficiency. To tackle the challenges, we propose a novel technique for effectively processing big graph data on Cloud. Specifically, the big data will be compressed with its spatiotemporal features on Cloud. By exploring spatial data correlation, we partition a graph data set into clusters. In a cluster, the workload can be shared by the inference based on time series similarity. By exploiting temporal correlation, in each time series or a single graph edge, temporal data compression is conducted. A novel data driven scheduling is also developed for data processing optimization. The experiment results demonstrate that the spatiotemporal compression and scheduling achieve significant performance gains in terms of data size and data fidelity loss.
Journal of Computer and System Sciences 01/2014; · 1.00 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Blockmodelling is an important technique in social network analysis for discovering the latent structure in graphs. A blockmodel partitions the set of vertices in a graph into groups, where there are either many edges or few edges between any two groups. For example, in the reply graph of a question and answer forum, blockmodelling can identify the group of experts by their many replies to questioners, and the group of questioners by their lack of replies among themselves but many replies from experts. Non-negative matrix factorisation has been successfully applied to many problems, including blockmodelling. However, these existing approaches can fail to discover the true latent structure when the graphs have strong background noise or are sparse, which is typical of most real graphs. In this paper, we propose a new non-negative matrix factorisation approach that can discover blockmodels in sparse and noisy graphs. We use synthetic and real datasets to show that our approaches have much higher accuracy and comparable running times.
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management; 10/2013
[Show abstract][Hide abstract] ABSTRACT: Retinal arteriovenous nicking (AV nicking) is the phenomenon where the venule is compressed or decreases in its caliber at both sides of an arteriovenous crossing. Recent research suggests that retinal AVN is associated with hypertension and cardiovascular diseases such as stroke. In this article, we propose a computer method for assessing the severity level of AV nicking of an artery-vein (AV) crossing in color retinal images. The vascular network is first extracted using a method based on multi-scale line detection. A trimming process is then performed to isolate the main vessels from unnecessary structures such as small branches or imaging artefact. Individual segments of each vessel are then identified and the vein is recognized through an artery-vein identification process. A vessel width measurement method is devised to measure the venular caliber along its two segments. The vessel width measurements of each venular segment is then analyzed and assessed separately and the final AVN index of a crossover is computed as the most severity of its two segments. The proposed technique was validated on 69 AV crossover points of varying AV nicking levels extracted from retinal images of the Singapore Malay Eye Study (SiMES). The results show that the computed AVN values are highly correlated with the manual grading with a Spearman correlation coefficient of 0.70. This has demonstrated the accuracy of the proposed method and the feasibility to develop a computer method for automatic AV nicking detection. The quantitative measurements provided by the system may help to establish a more reliable link between AV nicking and known systemic and eye diseases, which deserves further examination and exploration.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:5865-5868.
[Show abstract][Hide abstract] ABSTRACT: In this paper an automated method is presented for the detection of Focal Arteriolar Narrowing (FAN), a precursor for hypertension, stroke and other cardiovascular diseases. Our contribution in this paper is that, we have proposed a novel retinal blood vessel tracing and vessel width measurement algorithm, which is fully automated. We developed a novel method to detect FAN affected vessel segments by analysing their width distribution pattern. For initial results and quantitative evaluation of the proposed method, we evaluate our method on 30 color retinal images which are randomly selected by an experienced grader from SiMES dataset. We achieved the sensitivity of 75% and specificity of 98% in detecting FAN affected vessel segments and sensitivity of 80% and specificity of 86% in identifying healthy and FAN affected images. The acquired result shows the potential suitability of the proposed method for assisting the ophthalmologist to detect the FAN in clinical practice.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:7376-7379.
[Show abstract][Hide abstract] ABSTRACT: Age-related macular degeneration (AMD) is a major cause of visual impairment in the elderly and identifying people with the early stages of AMD is important when considering the design and implementation of preventative strategies for late AMD. Quantification of drusen size and total area covered by drusen is an important risk factor for progression. In this paper, we propose a method to detect drusen and quantify drusen size along with the area covered with drusen in macular region from standard color retinal images. We used combined local intensity distribution, adaptive intensity thresholding and edge information to detect potential drusen areas. The proposed method detected the presence of any drusen with 100% accuracy (50/50 images). For drusen detection accuracy (DDA), the segmentations produced by the automated method on individual images achieved mean sensitivity and specificity values of 74.94% and 81.17%, respectively.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:7392-7395.
[Show abstract][Hide abstract] ABSTRACT: Recent research suggests that retinal vessel caliber (or cross-sectional width) measured from retinal photographs is an important feature for predicting cardiovascular diseases (CVDs). One of the most utilized measures is to quantify retinal arteriolar and venular caliber as the Central Retinal Artery Equivalent (CRAE) and Central Retinal Vein Equivalent (CRVE). However, current computer tools utilize manual or semi-automatic grading methods to estimate CRAE and CRVE. These methods involve a significant amount of grader's time and can add a significant level of inaccuracy due to repetitive nature of grading and intragrader distances. An automatic and time efficient grading of the vessel caliber with highly repeatable measurement is essential, but is technically challenging due to a substantial variation of the retinal blood vessels' properties. In this paper, we propose a new technique to measure the retinal vessel caliber, which is an "edge-based" vessel tracking method. We measured CRAE and CRVE from each of the vessel types. We achieve very high accuracy (average 96.23%) for each of the cross-sectional width measurement compared to manually graded width. For overall vessel caliber measurement accuracy of CRAE and CRVE, we compared the results with an existing semi-automatic method which showed high correlation of 0.85 and 0.92, respectively. The intra-grader reproducibility of our method was high, with the correlation coefficient of 0.881 for CRAE and 0.875 for CRVE.
Computer methods and programs in biomedicine 03/2013; · 1.56 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper, we propose a new type of adaptive fuzzy inference system with a view to achieve improved performance for forecasting nonlinear time series data by dynamically adapting the fuzzy rules with arrival of new data. The structure of the fuzzy model utilized in the proposed system is developed based on the log-likelihood value of each data vector generated by a trained Hidden Markov Model. As part of its adaptation process, our system checks and computes the parameter values and generates new fuzzy rules as required, in response to new observations for obtaining better performance. In addition, it can also identify the most appropriate fuzzy rule in the system that covers the new data; and thus requires to adapt the parameters of the corresponding rule only, while keeping the rest of the model unchanged. This intelligent adaptive behavior enables our adaptive fuzzy inference system (FIS) to outperform standard FISs. We evaluate the performance of the proposed approach for forecasting stock price indices. The experimental results demonstrate that our approach can predict a number of stock indices, e.g., Dow Jones Industrial (DJI) index, NASDAQ index, Standard and Poor500 (S&P500) index and few other indices from UK (FTSE100), Germany (DAX) , Australia (AORD) and Japan (NIKKEI) stock markets, accurately compared with other existing computational and statistical methods.
[Show abstract][Hide abstract] ABSTRACT: We discuss a new formulation of a fuzzy validity index that generalizes the Newman-Girvan (NG) modularity function. The NG function serves as a cluster validity functional in community detection studies. The input data is an undirected weighted graph that represents, e.g., a social network. Clusters correspond to socially similar substructures in the network. We compare our fuzzy modularity with two existing modularity functions using the well-studied Karate Club and American College Football datasets.
IEEE Transactions on Fuzzy Systems 01/2013; 21(6). · 5.48 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Cloud computing opens a new era in IT as it can provide various elastic and scalable IT services in a pay-as-you-go fashion. In this philosophy, users of cloud storage services no longer physically maintain direct control over their data, which makes data security one of the major concerns of using cloud. Existing research work already allows data integrity to be verified without possession of the actual data file. However, such schemes in existence suffer from several common drawbacks. In this paper, we provide a formal analysis for possible types of fine-grained data updates and propose a scheme that can fully support authorized auditing and fine-grained update requests. Based on our scheme, we also propose a enhancement that can dramatically reduce communication overheads for verifying small updates. Theoretical analysis and experimental results demonstrate that our scheme can offer not only enhanced security and flexibility, but also significantly lower overhead for scenarios with a large number of frequent small updates, such as applications in social media and business transactions.
IEEE Transactions on Parallel and Distributed Systems 01/2013; · 1.80 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Retinal color imaging has enabled the identification and quantification of retinal vascular features such as vessel caliber, tortuosity and branching angle, etc. Studies have shown that these vascular features have association of pre-clinical or clinical cardiovascular diseases (CVD). In this paper, we discuss the automated measurement of retinal vascular features such as vessel width or caliber, vessel bifurcation and crossover point and vessel tortuosity. Vessel segmentation is the most important step for accurate and efficient vascular feature analysis. Therefore, we have proposed a method for retinal blood vessel segmentation to achieve high accuracy and efficiency. We introduce the existing techniques and their limitations. Following this, we discuss our proposed methods and analysis techniques to extract these vascular features from color retinal images. We also evaluated the results on our proposed feature extraction techniques by comparing other state of art methods.
Biosignals and Biorobotics Conference (BRC); 01/2013
[Show abstract][Hide abstract] ABSTRACT: In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible.
[Show abstract][Hide abstract] ABSTRACT: Retinal vascular branch and crossover points are unique features for each individual that can be used as a reliable biometric for personal authentication and can be used for information retrieval and security application. In this work, a novel biometric authentication scheme is proposed based on the retinal vascular network features. We apply an automatic technique to detect and identify retinal vascular branch and crossover points. These branch and crossover points are mapped from prominent blood vessels in the image. For this, a novel vessel width measurement method is applied and vessels more than certain widths are selected. Based on these vessel segments their corresponding branch and crossover points are identified. Invariant features are constructed through Geometric Hashing of the detected branch and crossover points. We consider the crossover points for modelling a basis pair and all other points together for locations in the hash table entries. Thus, the models are invariant to rotation, translation and scaling. For each person, the system is trained with the models to accept or reject a claimed identity. The initial results show that the proposed method has achieved 100% detection accuracy which is highly potential for reliable person identification.
Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference on; 01/2013
[Show abstract][Hide abstract] ABSTRACT: We discuss a new formulation of a fuzzy validity index that generalizes the Newman-Girvan (NG) modularity function. The NG function serves as a cluster validity functional in community detection studies. The input data is an undirected graph G = (V, E) that represents a social network. Clusters in V correspond to socially similar substructures in the network. We compare our fuzzy modularity to an existing modularity function using the well-studied Karate Club data set.
Fuzzy Systems (FUZZ), 2013 IEEE International Conference on; 01/2013
[Show abstract][Hide abstract] ABSTRACT: Traditional classification methods assume that the training and the test data arise from the same underlying distribution. However in some adversarial settings, the test set can be deliberately constructed in order to increase the error rates of a classifier. A prominent example is email spam where words are transformed to avoid word-based features embedded in a spam filter. Recent research has modeled interactions between a data miner and an adversary as a sequential Stackelberg game, and solved its Nash equilibrium to build classifiers that are more robust to subsequent manipulations on training data sets. However in this paper we argue that the iterative algorithm used in the Stackelberg game, which solves an optimization problem at each step of play, is sufficient but not necessary for achieving Nash equilibria in classification problems. Instead, we propose a method that transforms singular vectors of a training data matrix to simulate manipulations by an adversary, and from that perspective a Nash equilibrium can be obtained by solving a novel optimization problem only once. We show that compared with the iterative algorithm used in recent literature, our one-step game significantly reduces computing time while still being able to produce good Nash equilibria results.
Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence; 12/2012
[Show abstract][Hide abstract] ABSTRACT: Sentiment analysis of documents aims to characterise the positive or negative sentiment expressed in documents. It has been formulated as a supervised classification problem, which requires large numbers of labelled documents. Semi-supervised sentiment classification using limited documents or words labelled with sentiment-polarities are approaches to reducing labelling cost for effective learning. Expectation Maximisation (EM) has been widely used in semi-supervised sentiment classification. A prominent problem with existing EM-based approaches is that the objective function of EM may not conform to the intended classification task and thus can result in poor classification performance. In this paper we propose to augment EM with the lexical knowledge of opinion words to mitigate this problem. Extensive experiments on diverse domains show that our lexical EM algorithm achieves significantly higher accuracy than existing standard EM-based semi-supervised learning approaches for sentiment classification, and also significantly outperforms alternative approaches using the lexical knowledge.
Proceedings of the 13th international conference on Web Information Systems Engineering; 11/2012
[Show abstract][Hide abstract] ABSTRACT: BACKGROUND: Searching for structural motifs across known protein structures can be useful for identifying unrelated proteins with similar function and characterising secondary structures such as beta-sheets. This is infeasible using conventional sequence alignment because linear protein sequences do not contain spatial information. beta-residue motifs are beta-sheet substructures that can be represented as graphs and queried using existing graph indexing methods, however, these approaches are designed for general graphs that do not incorporate the inherent structural constraints of beta-sheets and require computationally-expensive filtering and verification procedures. 3D substructure search methods, on the other hand, allow beta-residue motifs to be queried in a three-dimensional context but at significant computational costs. RESULTS: We developed a new method for querying beta-residue motifs, called BetaSearch, which leverages the natural planar constraints of beta-sheets by indexing them as 2D matrices, thus avoiding much of the computational complexities involved with structural and graph querying. BetaSearch demonstrates faster filtering, verification, and overall query time than existing graph indexing approaches whilst producing comparable index sizes. Compared to 3D substructure search methods, BetaSearch achieves 33 and 240 times speedups over index-based and pairwise alignment-based approaches, respectively. Furthermore, we have presented case-studies to demonstrate its capability of motif matching in sequentially dissimilar proteins and described a method for using BetaSearch to predict beta-strand pairing. CONCLUSIONS: We have demonstrated that BetaSearch is a fast method for querying substructure motifs. The improvements in speed over existing approaches make it useful for efficiently performing high-volume exploratory querying of possible protein substructural motifs or conformations. BetaSearch was used to identify a nearly identical beta-residue motif between an entirely synthetic (Top7) and a naturally-occurring protein (Charcot-Leyden crystal protein), as well as identifying structural similarities between biotin-binding domains of avidin, streptavidin and the lipocalin gamma subunit of human C8. AVAILABILITY: The web-interface, source code, and datasets for BetaSearch can be accessed from http://www.csse.unimelb.edu.au/~hohkhkh1/betasearch.
[Show abstract][Hide abstract] ABSTRACT: This paper investigates software fault localization methods which are based on program spectra -- data on execution profiles from passed and failed tests. We examine a standard method of spectral fault localization: for each statement we determine the number of passed and failed tests in which the statement was/wasn't executed and a function, or metric, of these four values is used to rank statements according to how likely they are to be buggy. Many different metrics have been used. Here our main focus is to determine how much improvement in performance could be achieved by finding better metrics. We define the cost of fault localization using a given metric and the unavoidable cost, which is independent of the choice of metric. We define a class of strictly rational metrics and argue that is reasonable to restrict attention to these metrics. We show that every single bug optimal metric performs as well as any strictly rational metric for single bug programs, and the resulting cost is the unavoidable cost. We also show how any metric can be adapted so it is single bug optimal, and give results of empirical experiments using single- and two-bug programs.
Proceedings of the Thirty-fifth Australasian Computer Science Conference - Volume 122; 01/2012
[Show abstract][Hide abstract] ABSTRACT: Blockmodelling is an important technique for decomposing graphs into sets of roles. Vertices playing the same role have similar patterns of interactions with vertices in other roles. These roles, along with the role to role interactions, can succinctly summarise the underlying structure of the studied graphs. As the underlying graphs evolve with time, it is important to study how their blockmodels evolve too. This will enable us to detect role changes across time, detect different patterns of interactions, for example, weekday and weekend behaviour, and allow us to study how the structure in the underlying dynamic graph evolves. To date, there has been limited research on studying dynamic blockmodels. They focus on smoothing role changes between adjacent time instances. However, this approach can overfit during stationary periods where the underling structure does not change but there is random noise in the graph. Therefore, an approach to a) find blockmodels across spans of time and b) to find the stationary periods is needed. In this paper, we propose an information theoretic framework, SeqiBloc, combined with a change point detection approach to achieve a) and b). In addition, we propose new vertex equivalence definitions that include time, and show how they relate back to our information theoretic approach. We demonstrate their usefulness and superior accuracy over existing work on synthetic and real datasets.
[Show abstract][Hide abstract] ABSTRACT: The robustness of a schedule, with respect to its probability of successful execution, becomes an indispensable requirement in open and dynamic service-oriented environment, such as grids or clouds. We design a fine-grained risk assessment model customized for workflows to precisely compute the cost of failure of a schedule. In comparison with current course-grained model, ours takes the relation of task dependency into consideration and assigns higher impact factor to tasks at the end. Thereafter, we design the utility function with the model and apply a genetic algorithm to find the optimized schedule, thereby maximizing the robustness of the schedule while minimizing the possible risk of failure. Experiments and analysis show that the application of customized risk assessment model into scheduling can generally improve the successful probability of a schedule while reducing its exposure to the risk.
Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International; 01/2012