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In image restoration, the region of support of the point spread function is often much smaller than the size of the observed degraded image and this property is utilized in many image deconvolution algorithms. For the constrained total least squares (CTLS)-based algorithm, it means that the solution of the CTLS algorithm should retain the block-circulant and sparse structure of the degradation matrix simultaneously. In real image restoration problems, the CTLS method often involves large-scale computation and is often solved using Mesarovic et al.’s algorithm. However, there is concern about whether their algorithm preserves the sparse structure of the degradation matrix. In this paper, we prove that by imposing an extra constraint, the sparse structure in their algorithm can be preserved. Then, we use the projection onto convex sets algorithm to find a solution to this extended formulation. Our experimental study indicates that the proposed method performs competitively, and often better, in terms of visual and objective evaluations.

Accurate segmentation of magnetic resonance (MR) images of the brain is of interest in the study of many brain disorders. In this paper, we provide a review of some of the current approaches in the tissue segmentation of MR brain images. We broadly divided current MR brain image segmentation algorithms into three categories: classification- based, region-based, and contour-based, and discuss the advantages and disadvantages of these approaches. We also briefly review our recent work in this area. We show that by incorporating two key ideas into the conventional fuzzy c- means clustering algorithm, we are able to take into account the local spatial context and compensate for the intensity nonuniformity (INU) artifact during the clustering process. We conclude this review by pointing to some possible future directions in this area.

Kinesin is a walking motor protein that shuttles cellular cargoes along microtubules (MTs). This protein is considered as an information processor capable of sensing cellular inputs and transforming them into mechanical steps. Here, we propose a computational model to describe the mechanochemical kinetics underlying forward and backward stepping behavior of kinesin motor as a digital circuit designed based on an adenosine triphosphate (ATP)-driven finite state machine. Kinetic analysis suggests that the backward stepping of kinesin is mainly driven by ATP hydrolysis, whereas ATP synthesis rises the duration of this stepping. It is shown that kinesin pausing due to waiting for ATP binding at limiting ATP concentration ([ATP]) and low backward loads could be longer than that caused by low rate of ATP synthesis under high backward loads. These findings indicate that the pausing duration of kinesin in MT-bound (M·K) kinetic state is affected by [ATP], which in turn affects its velocity at fixed loads. We show that the proposed computational model accurately simulates the forward and backward stepping behavior of kinesin motor under different [ATP] and loads.

Motivation: Cellular interactions of kinesin-1, an adenosine triphosphate (ATP)-driven motor protein capable of undergoing multiple steps on a microtubule (MT), affect its mechanical processivity, the number of steps taken per encounter with MT. Even though the processivity of kinesin has been widely studied, a detailed study of the factors that affect the stepping of the motor along MT is still lacking. Results: We model the cellular interactions of kinesin as a probabilistic timed automaton and use the model to simulate the mechanical processivity of the motor. Theoretical analysis suggests: (i) backward stepping tends to be powered by ATP hydrolysis, rather than ATP synthesis, (ii) backward stepping powered by ATP synthesis is more likely to happen with limiting ATP concentration ([ATP]) at high loads and (iii) with increasing load the frequency of backward stepping powered by ATP hydrolysis at high [ATP] is greater than that powered by ATP synthesis at limiting [ATP]. Together, the higher frequency of backward stepping powered by ATP hydrolysis than by ATP synthesis is found to be a reason for the more dramatic falling of kinesin processivity with rising load at high [ATP] compared with that at low [ATP]. Simulation results further show that the processivity of kinesin can be determined by the number of ATP hydrolysis and synthesis kinetic cycles taken by the motor before becoming inactive. It is also found that the duration of a backward stepping cycle at high loads is more likely to be less than that of a forward stepping cycle. Contact: h.r.khataee@griffithuni.edu.au or a.liew@griffith.edu.au .

The emerging field of bioinformatics has recently created much interest in the computer science and engineering communities. With the wealth of sequence data in many public online databases and the huge amount of data generated from the Human Genome Project, computer analysis has become indispensable. This calls for novel algorithms and opens up new areas of applications for many pattern recognition techniques. In this article, we review two major avenues of research in bioinformatics, namely DNA sequence analysis and DNA microarray data analysis. In DNA sequence analysis, we focus on the topics of sequence comparison and gene recognition. For DNA microarray data analysis, we discuss key issues such as image analysis for gene expression data extraction, data pre-processing, clustering analysis for pattern discovery and gene expression time series data analysis. We describe current methods and show how computational techniques could be useful in these areas. It is our hope that this review article could demonstrate how the pattern recognition community could have an impact on the fascinating and challenging area of genomic research.

An image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. In the conventional FCM clustering algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and does not take into consideration the spatial distribution of pixels in an image. By introducing a novel dissimilarity index in the modified FCM objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus exploiting the high inter-pixel correlation inherent in most real-world images. The incorporation of local spatial continuity allows the suppression of noise and helps to resolve classification ambiguity. To account for smooth intensity variation within each homogenous region in an image, a multiplicative field is introduced to each of the fixed FCM cluster prototype. The multiplicative field effectively makes the fixed cluster prototype adaptive to slow smooth within-cluster intensity variation, and allows homogenous regions with slow smooth intensity variation to be segmented as a whole. Experimental results with synthetic and real color images have shown the effectiveness of the proposed algorithm.

In blocking artifacts reduction based on the projection onto convex sets (POCS) technique, good constraint sets are very important. Until recently, smoothness constraint sets (SCS) are often formulated in the image domain, whereas quantization constraint set is defined in the block-based discrete cosine transform (BDCT) domain. Thus, frequent BDCT transform is inevitable in alternative projections. In this paper, based on signal and quantization noise statistics, we proposed a novel smoothness constraint set in the BDCT transform domain via the Wiener filtering concept. Experiments show that POCS using this smoothness constraint set not only has good convergence but also has better objective and subjective performance. Moreover, this set can be used as extra constraint set to improve most existing POCS-based image postprocessing methods.

The rapid advancement of DNA microarray technology has revolutionalized genetic research in bioscience. Due to the enormous amount of gene expression data generated by such technology, computer processing and analysis of such data has become indispensable. In this paper, we present a computational framework for the extraction, analysis and visualization of gene expression data from microarray experiments. A novel, fully automated, spot segmentation algorithm for DNA microarray images, which makes use of adaptive thresholding, morphological processing and statistical intensity modeling, is proposed to: (i) segment the blocks of spots, (ii) generate the grid structure, and (iii) to segment the spot within each subregion. For data analysis, we propose a binary hierarchical clustering (BHC) framework for the clustering of gene expression data. The BHC algorithm involves two major steps. Firstly, the fuzzy C-means algorithm and the average linkage hierarchical clustering algorithm are used to split the data into two classes. Secondly, the Fisher linear discriminant analysis is applied to the two classes to assess whether the split is acceptable. The BHC algorithm is applied to the sub-classes recursively and ends when all clusters cannot be split any further. BHC does not require the number of clusters to be known in advance. It does not place any assumption about the number of samples in each cluster or the class distribution. The hierarchical framework naturally leads to a tree structure representation for effective visualization of gene expressions.

It is well known that at low-bit-rate block discrete cosine transform compressed image exhibits visually annoying blocking and ringing artifacts. In this paper, we propose a noniterative, wavelet-based deblocking algorithm to reduce both types of artifacts. The algorithm exploits the fact that block discontinuities are constrained by the dc quantization interval of the quantization table, as well as the behavior of wavelet modulus maxima evolution across wavelet scales to derive appropriate threshold maps at different wavelet scales. Since ringing artifacts occur near strong edges, which can be located either along block boundaries or within blocks, suppression of block discontinuities does not always reduce ringing artifacts. By exploiting the behavior of ringing artifacts in the wavelet domain, we propose a simple yet effective method for the suppression of such artifacts. The proposed algorithm can suppress both block discontinuities and ringing artifacts effectively while preserving true edges and textural information. Simulation results and extensive comparative study with both iterative and noniterative methods reported in the literature have shown the effectiveness of our algorithm.

Motivation: Microarray time-series data provides us a possible means for identification of transcriptional regulation relationships among genes. Currently, the most commonly used method in determining whether or not two genes have a potential regulatory relationship is to measure their expressional similarity using Pearson's correlation coefficient. Although this traditional correlation method has been successfully applied to find functionally correlated genes, it does have many limitations. In the hope of overcoming such circumstances and getting more insights into the transcriptional regulatory issue, we propose an autoregressive (AR)-based technique for detection of potential regulated gene pairs from time-series microarray measurements. Results: We use the well-known AR modeling technique to characterize temporal gene expression data from the Spellman's α-synchronized yeast cell-cycle experiment. In this method, time-series expression profiles are decomposed into spectral components and correlations between profiles are then computed in a component-wise sense. We show how these component-wise correlations reveal possible regulatory relationships. Our technique is applied on known transcriptional regulations and is able to identify many of those missed by the traditional correlation method.

During image deblocking, the preservation of visually significant edges in the image is important. Based on the observation that blocking artifact along edge direction is difficult for human perception, a number of existing techniques have proposed to filter the edge pixels with low weight or bypass them altogether to avoid blurring. However, the un-processed artifacts that are anti-parallel to the edge direction often fragment the edges and seriously degrade visual quality. In this paper, by considering the behavior of intensity evolution along and across edges, we propose a new POCS-based algorithm using a new smoothness constraint set. The new constraint set is realized indirectly by limiting the distance between the coded image and the image simulated using edge-adaptive quadrangle meshes. Experimental results indicated that our method compares favorably with several existing techniques.

An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input images may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. The INU artifact is formulated as a multiplicative bias field affecting the true MR imaging signal. By modeling the log bias field as a stack of smoothing B-spline surfaces, with continuity enforced across slices, the computation of the 3-D bias field reduces to that of finding the B-spline coefficients, which can be obtained using a computationally efficient two-stage algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.

Gene expression data analysis has recently emerged as an active area of research. An important tool for unsupervised analysis of gene expression data is cluster analysis. Although many clustering algorithms have been proposed for such task, problems such as estimating the right number of clusters and adapting to different cluster characteristics are still not satisfactorily addressed. In this paper, we propose a binary hierarchical clustering (BHC) algorithm for the clustering of gene expression data. The BHC algorithm involves two major steps: (i) the fuzzy C-means algorithm and the average linkage hierarchical clustering algorithm are used to partition the data into two classes, and (ii) the Fisher linear discriminant analysis is applied to the two classes to refine and assess whether the partition is acceptable. The BHC algorithm recursively partitions the subclasses until all clusters cannot be partition any further. It does not require the number of clusters to be supplied in advance nor does it place any assumption about the size of each cluster or the class distribution. The BHC algorithm naturally leads to a tree structure representation, where the clustering results can be visualized easily.

The large number of bases in a DNA sequence and the cryptic nature of the 4-alphabet representation make graphical visualization of DNA sequences useful for biologists. However, existing 3D graphical representations are complicated, whereas existing 2D graphical representations suffer from high degeneracy, and many features in a DNA sequence cannot be visualized clearly. This Letter introduces a novel 2D method of DNA representation: the DB-Curve (Dual-Base Curve), which overcomes some of the limitations in existing 2D graphical representations. Many properties of DNA sequences can be observed and visualized easily using a combination of DB-Curves. The new representation can avoid degeneracy completely compared to existing 2D graphical representations of DNA sequences. Unlike 3D graphical representations, no 2D projection is required for the DB-Curve, and this allows for easier analysis of DNA sequences. The DB-Curve provides a useful graphical tool for the visualization and study of DNA sequences.

Recent research has shown that the speaker's lip shape and movement contain rich identity-related information and can be adopted for speaker identification and authentication. Among all the static lip features, the lip texture (intensity variation inside the outer lip contour) is of high discriminative power to differentiate various speakers. However, the existing lip texture feature representations cannot describe the texture information adequately and provide unsatisfactory identification results. In this paper, a sparse representation of the lip texture is proposed and a corresponding visual speaker identification scheme is presented. In the training stage, a sparse dictionary is built based on the texture samples for each speaker. In the testing stage, for any lip image investigated, the lip texture information is extracted and the reconstruction errors using all the dictionaries for every speaker are calculated. The lip image is identified to the speaker with the minimum reconstruction error. The experimental results show that the proposed sparse coding based scheme can achieve much better identification accuracy (91.37% for isolate image and 98.21% for image sequence) compared with several state-of-the-art methods when considering the lip texture information only.

In unsupervised data mining, one is usually interested in discovering groups of data that exhibit certain kind of coherency. A classical technique for unsupervised data partitioning is cluster analysis, where objects are sorted into groups in such a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise. Cluster analysis has been applied to many classification problems. In (Wu, Liew, & Yan, 2004), clustering is applied to find natural groupings in the data. In (Borland, Hirschberg, & Lye, 2001), clustering is used for data reduction, where a group of similar objects is summarized by a representative sample in the group. Recently, clustering has been applied extensively in gene expression data analysis. In gene expression data, the objects along the row dimension correspond to genes or some DNA sequence, and the attributes in the column dimension correspond to cDNA microarray experiments or time point samples. Clustering in the row direction, or gene-wise clustering, has been done, for example, on the Yeast gene expression data and human cell (Spellman, Sherlock, Zhang, et al., 1998; Eisen, Spellman, Brown, & Botstein, 1998), whereas clustering in the column direction, or sample-wise clustering, has been done, for example, on cancer type classification (Golub, Slonim, Tamayo, et al., 1999) (Klein, Tu, Stolovitzky, et al., 2001). However, in many real world data, not all attributes of an object are relevant in grouping the objects into meaningful classes. In many cases, some attributes are relevant to only some of the clusters and different clusters may have different relevant subsets of attributes. By relaxing the constraint that related objects must behave similarly across the entire set of attributes, biclustering considers only a relevant subset of attributes when looking for similarity between objects. In this article, we give an overview of the biclustering problem, discuss some common biclustering algorithms, and highlight some interesting applications of biclustering.

In many JPEG image splicing forgeries, the tampered image patch has been JPEG-compressed twice with different block alignments. Such phenomenon in JPEG image forgeries is called the shifted double JPEG (SDJPEG) compression effect. Detection of SDJPEG-compressed patches could help in detecting and locating the tampered region. However, the current SDJPEG detection methods do not provide satisfactory results especially when the tampered region is small. In this paper, we propose a new SDJPEG detection method based on an adaptive discrete cosine transform (DCT) coefficient model. DCT coefficient distributions for SDJPEG and non-SDJPEG patches have been analyzed and a discriminative feature has been proposed to perform the two-class classification. An adaptive approach is employed to select the most discriminative DCT modes for SDJPEG detection. The experimental results show that the proposed approach can achieve much better results compared with some existing approaches in SDJPEG patch detection especially when the patch size is small.

In high-throughput applications, accurate segmentation of biomedical images can be considered as an important step for recognizing cells that have the phenotype of interest. In this paper, while conventional fuzzy clustering is not able to implement the local and global spatial information, a novel spatial fuzzy clustering cell image segmentation algorithm is proposed. The segmentation procedure is divided into two stages: the first stage involves processing the local and global spatial information of the given cell image and a final segmentation stage which is based on the idea of conventional fuzzy clustering. Our idea can be considered as a sequential integration of region based methods and fuzzy clustering for cell image segmentation. Experimental results show that the proposed model yields significantly better performance in comparison with several existing methods.

This paper introduces a mechanism to learn optimal classifier combining algorithms for an ensemble system. By using a genetic algorithm approach that focuses on 3 objectives namely the number of correct classified observations, the number of selected features and the number of selected classifiers, optimal solution can be achieved after several interactions of crossover and mutation. We also employ the Ordered Weighted Averaging operator in which a weight vector is built by a Linear Decreasing (LD) function to find average values of outputs from combining algorithms. Experiments on 2 well-known UCI Machine Learning Repository datasets demonstrate benefits of our approach compared with other state-of-the-art ensemble methods like Decision Template, SCANN and all fixed combining algorithms in the ensemble system.

Clustering methods classify patterns into clusters using the entire set of attributes of patterns in the similarity measurement. In plenty of cases, patterns are similar under a subset of attributes only. The class of methods that cluster patterns based on subsets of attributes is called biclustering. Biclustering simultaneously groups on both rows and columns of a data matrix and has been applied to various fields, especially gene expression data. However, the biclustering problem is inherently intractable and computationally complex. In recent years, several biclustering algorithms which are based on linear coherent model have been proposed. In this paper, we introduce a novel GA-based algorithm that uses hyperplane to describe the linear relationships between rows (genes) in a sub-matrix (bicluster). The performance of our algorithm is tested via simulated data, gene expression data and compared with several other bicluster methods.

In this paper we introduce a novel approach for classifier and feature selection in a multi-classifier system using Genetic Algorithm (GA). Specifically, we propose a 2-part structure for each chromosome in which the first part is encoding for classifier and the second part is encoding for feature. Our structure is simple in the implementation of the crossover as well as the mutation stage of GA. We also study 8 different fitness functions for our GA based algorithm to explore the optimal fitness functions for our model. Experiments are conducted on both 14 UCI Machine Learning Repository and CLEF2009 medical image database to demonstrate the benefit of our model on reducing classification error rate.

Death, injury and disability from road traffic crashes continue to be a major global public health problem. Recent data suggest that the number of fatalities from traffic crashes is in excess of 1.25 million people each year with non-fatal injuries affecting a further 20-50 million people. It is predicted that by 2030 road traffic accidents will have progressed to be the 5th leading cause of death and that the number of people who will die annually from traffic accidents will have doubled from current levels. Therefore, methods to reduce accident severity are of great interest to traffic agencies and the public at large. Road accident fatality rate depends on many factors and it is a very challenging task to investigate the dependencies between the attributes because of the many environmental and road accident factors. Any missing data in the database could obscure the discovery of important factors and lead to invalid conclusions. In order to make the traffic accident datasets useful for analysis, it should be preprocessed properly. In this paper, we present a novel method based on sampling of distributions obtained from correlation measures for the imputation of missing values to improve the quality of the traffic accident data. We evaluated our algorithm using two publicly available traffic accident databases of United States (explore.data.gov, data. opencolorado.org). Our results indicate that the proposed method performs significantly better than the three existing algorithms.

In pattern classification, when the feature space is of high dimensionality or patterns are “similar” on a subset of features only, the traditional clustering methods do not show good performance. Biclustering is a class of methods that simultaneously carry out grouping on two dimensions and has many applications to different fields, especially gene expression data analysis. Because of simultaneous classification on both rows and columns of a data matrix, the biclustering problem is inherently intractable and computationally complex. One of the most complex models in biclustering problem is linear coherent model. Several biclustering algorithms based on this model have been proposed in recent years. However, none of them is able to perfectly recognize all linear patterns in a bicluster. In this work, we propose a novel algorithm based on Hough transform that can find all linear coherent patterns. In the sequel we apply it to gene expression data.

Road traffic accidents are a major public health concern, resulting in an estimated 1.3 million deaths and 52 million injuries worldwide each year. All the developed and developing countries suffer from the consequences of increase in both human and vehicle population. Therefore, methods to reduce accident severity are of great interest to traffic agencies and the public at large. To analysis the traffic accident factors effectively we need a complete traffic accident historical database without missing data. Road accident fatality rate depends on many factors and it is a very challenging task to investigate the dependencies between the attributes because of the many environmental and road accident factors. Any missing data in the database could obscure the discovery of important factors and lead to invalid conclusions. In order to make the traffic accident datasets useful for analysis, it should be preprocessed properly. In this paper, we present a novel method based on decision tree and imputed value sampling based on correlation measure for the imputation of missing values to improve the quality of the traffic accident data. We applied our algorithm to the publicly available large traffic accident database of United States (explore.data.gov), which is the largest open federal database in United States. We compare our algorithm with three existing imputation methods using three evaluation criteria, i.e. mean absolute error, coefficient of determination and root mean square error. Our results indicate that the proposed method performs significantly better than the three existing algorithms.

The paper introduces a novel 2-Stage model for multi-classifier system. Instead of gathering posterior probabilities resulted from base classifiers into a single dataset called meta-data or Level1 data like in the original 2-Stage model, here we separate data in K Level1 matrices corresponding to the K base classifiers. These data matrices, in turn, are classified in sequence by a new classifier at the second stage to generate output of that new classifier called Level2 data. Next, Weight Matrix algorithm is proposed to combine Level2 data and produces prediction for unlabeled observations. Experimental results on CLEF2009 medical image database demonstrate the benefit of our model in comparison with several existing ensemble learning models.

Combining multiple classifiers to achieve better performance than any single classifier is one of the most important research areas in machine learning. In this paper, we focus on combining different classifiers to form an effective ensemble system. By introducing a novel framework operated on outputs of different classifiers, our aim is to build a powerful model which is competitive to other well-known combining algorithms such as Decision Template, Multiple Response Linear Regression (MLR), SCANN and fixed combining rules. Our approach is difference from the traditional approaches in that we use Gaussian Mixture Model (GMM) to model distribution of Level1 data and to predict the label of an observation based on maximizing the posterior probability realized through Bayes model. We also apply Principle Component Analysis (PCA) to output of base classifiers to reduce its dimension of what before GMM modeling. Experiments were evaluated on 21 datasets coming from University of California Irvine (UCI) Machine Learning Repository to demonstrate the benefits of our framework compared with several benchmark algorithms.

This paper introduces a novel 2-stage classification system with stacking and genetic algorithm (GA) based feature selection. Specifically, Level1 data is first generated by stacking on the original data (called Level0 data) with base classifiers. Level1data is then classified by a second classifier (denoted by C) with feature selection using GA. The advantage of applying GA on Level1 data is that it has lower dimension and is more uniformity than Level0 data. We conduct experiments on both 18 UCI data files and CLEF2009 medical image database to demonstrate superior performance of our model in comparison with several popular combining algorithms.

Combining outputs from different classifiers to achieve high accuracy in classification task is one of the most active research areas in ensemble method. Although many state-of-art approaches have been introduced, no one method performs the best on all data sources. With the aim of introducing an effective classification model, we propose a Gaussian Mixture Model (GMM) based method that combines outputs of base classifiers (called meta-data or Level1 data) resulted from Stacking Algorithm. We further apply Genetic Algorithm (GA) to that data as a feature selection strategy to explore an optimal subset of Level1 data in which our GMM-based approach can achieve high accuracy. Experiments on 21 UCI Machine Learning Repository data files and CLEF2009 medical image database demonstrate the advantage of our framework compared with other well-known combining algorithms such as Decision Template, Multiple Response Linear Regression (MLR), SCANN and fixed combining rules as well as GMM-based approaches on original data.

High quality bilingual dictionaries are rarely available for lower-density language pairs, especially for those that are closely related. Using a third language as a pivot to link two other languages is a well-known solution, and usually requires only two input bilingual dictionaries to automatically induce the new one. This approach, however, produces many incorrect translation pairs because the dictionary entries are normally are not transitive due to polysemy and the ambiguous words in the pivot language. Utilizing the complete structures of the input bilingual dictionaries positively influences the result since dropped meanings can be countered. Moreover, an additional input dictionary may provide more complete information for calculating the semantic distance between word senses which is key to suppressing wrong sense matches. This paper proposes an extended constraint optimization model to inducing new dictionaries of closely related languages from multiple input dictionaries, and its formalization based on Integer Linear Programming. Evaluations indicated that the proposal not only outperforms the baseline method, but also shows improvements in performance and scalability as more dictionaries are utilized.

This paper introduces a novel framework that uses fuzzy IF-THEN rules in an ensemble system. Our model tackles several drawbacks. First, IF-THEN rules approaches have problems with high dimensional data since computational cost is exponential. In our framework, rules are operated on outputs of base classifiers which frequently have lower dimensionality than the original data. Moreover, outputs of base classifiers are scaled within the range [0, 1] so it is convenient to apply fuzzy rules directly instead of requiring data transformation and normalization before generating fuzzy rules. The performance of this model was evaluated through experiments on 6 commonly used datasets from UCI Machine Learning Repository and compared with several state-of-art combining classifiers algorithms and fuzzy IF-THEN rules approaches. The results show that our framework can improve the classification accuracy.

Image segmentation has been one of the most important unsolved problems in computer vision for many years. Recently, there have been great effort in producing better segmentation algorithms. The purpose of this paper is to introduce two proposed graph based segmentation methods, namely, graph-cut models (deterministic) and a unified graphical model (probabilistic). We present some foreground/background segmentation results to illustrate the performance of the algorithms on images with complex background scene.

In this paper, we present a novel modified Fuzzy C-means algorithm with symmetry information to reduce the effect of noise in brain tissue segmentation in magnetic resonance image (MRI). We integrate brain's bilateral symmetry into the conventional Fuzzy C-means (FCM) as an additional term. In experiments, some synthetic images, and both simulated and real brain images were used to investigate the robustness of the method against noise. Finally, the method was compared with the conventional FCM algorithm. Results show the viability of the approach and the preliminary investigation appears promising.

In this study a new image segmentation framework which combines the Fuzzy c means clustering and the level set method is presented. Using this framework, the well-known Chan and Vese's level set technique and classical Bayes classifier are employed to obtain a prior membership value for each pixel based on region information. Next, a novel clustering model based on fuzzy c-mean clustering assisted by prior membership values is used to obtain the final segmentation. Experiments performed on high-throughput fluorescence microscopy colon cancer cell images, which are commonly utilized for the study of many normal and neoplastic procedures, indicate a significant improvement in accuracy when compared to several existing techniques.

This paper presents a new modified fuzzy c-means (FCM) clustering algorithm that exploits bilateral symmetry information in image data. With the assumption of pixels that are located symmetrically tend to have similar intensity values; we compute the degree of symmetry for each pixel with respect to a global symmetry axis of the image. This information is integrated into the objective function of the standard FCM algorithm. Experimental results show the effectiveness of the approach. The method was further improved using neighbourhood information, and was compared with conventional fuzzy c-means algorithms.

This paper presents an integration framework for image segmentation. The proposed method is based on Fuzzy c-means clustering (FCM) and level set method. In this framework, firstly Chan and Vese's level set method (CV) and Bayes classifier based on mixture of density models are utilized to find a prior membership value for each pixel. Then, a supervised kernel based fuzzy c-means clustering (SKFCM) algorithm assisted by prior membership values is developed for final segmentation. The performance of our approach has been evaluated using high-throughput fluorescence microscopy colon cancer cell images, which are commonly used for the study of many normal and neoplastic procedures. The experimental results show the superiority of the proposed clustering algorithm in comparison with several existing techniques.

In neuroimage analysis, the automatic identification of symmetry plane has various applications. Despite the considerable amount of research, this remains an open problem. Most of the existing work based on image intensity is either sensitive to strong noise or not applicable to different imaging modalities. This paper presents a novel approach for identifying symmetry plane in three-dimensional brain magnetic resonance (MR) images based on the concepts of fractal dimension and lacunarity analysis which characterizes the complexity and homogeneity of an object. Experimental results, evaluation, and comparison with two other state-of-the-art techniques show the accuracy and the robustness of our method.

Symmetry plane identification is a critical initial step in brain image analysis, either done manually or automatically. Automatic extraction of the symmetry plane can provide an initial estimate for brain image registration, pathology assessment and disease diagnosis. This paper presents a novel technique for extracting the mid plane from volumetric magnetic resonance images (MRI). It is a theoretically simple approach that utilizes anatomical and radiological properties. The method is straight-forward to implement without the need for any prior segmentation. The efficacy of the proposed method was tested on brain MRI while investigating the robustness against rotation, intensity non-uniformity, noise and pathology. The method was compared with a cross-correlation based technique and the results show the viability of the approach.

In brain image analysis, the automatic identification of symmetry plane has various applications. This paper presents a new method that uses the concept of fractal dimension as a quantitative measure for identifying symmetry plane in three-dimensional (3D) brain magnetic resonance (MR) images. The method was tested on various 3D MRI datasets. Robust and accurate results were obtained in our experiments.

Background Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner. Methods In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditions as seeds to create candidate bicluster tables. The tables have two columns: (a) a gene set, and (b) the conditions on which the gene set have dissimilar expression levels to the seed. First, the genes with less than the maximum number of dissimilar conditions are identified and a table of these genes is created. Second, the rows that have the same dissimilar conditions are grouped together. Third, the table is sorted in ascending order based on the number of dissimilar conditions. Finally, beginning with the first row of the table, a test is run repeatedly to determine whether the cardinality of the gene set in the row is greater than the minimum threshold number of genes in a bicluster. If so, a bicluster is outputted and the corresponding row is removed from the table. Repeating this process, all biclusters in the table are systematically identified until the table becomes empty. Conclusions This paper presents a novel biclustering algorithm for the identification of additive biclusters. Since it involves exhaustively testing combinations of genes and conditions, the additive biclusters can be found more readily.

Digital fingerprinting provides a means of tracing unauthorized re-distribution of digital objects. With an unique fingerprint being imperceptibly embedded in each authorized copy of the object, in case a pirate copy is found, by analysing the fingerprint in the observed pirate copy, the distributer can identify the users who produced the pirate copy. Collusion-secure fingerprinting schemes address the problem of collusion, where a group of users (a coalition) detect and change the fingerprint symbols in their copies before producing pirate copies. It has been proved that there exist collusion-secure fingerprinting schemes that can identify at least one member of the coalition for any reasonably sized coalition. In order to guarantee the quality of the object, short fingerprinting codes are preferred in practical applications. A lower bound on the code length has been derived by Peikert et al, that is, any collusion-secure fingerprinting codes must have length at least o(s2log(1/se)), where s is the size of coalition. Codes which achieve the lower bound are called optimal codes. However, currently known optimal codes do not have any efficient (polynomial time-complexity) tracing procedure to identify the coalition. The best known codes with efficient tracing algorithms, which were constructed by Cortrina-Navau and Fernandez in 2010, have length O(s6log(s/e)log(N)), where N is the total number of authorized users. In this paper, we construct a class of codes which have an efficient tracing algorithm and have length O(s2log(1/e)log(N)). Our codes are much shorter than those by Cortrina-Navau and Fernandez.

We show how the first order statistic, i.e. the histogram, of the wavelet filtered image is related to the higher order statistic of the original image. We then propose a texture classification method that uses the histogram information of the filtered images as the feature vector. The histogram based features are able to provide richer description of the texture information. Classification experiments show that the proposed method achieves significantly better classification result than the traditional Gabor wavelet filtering method that uses only the mean and standard deviation of the filtered image as feature vectors.

In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits a consistent pattern over a subset of conditions. Although used extensively in gene expression data analysis, conventional clustering algorithms that consider the entire row or column in an expression matrix can therefore fail to detect useful patterns in the data. Recently, biclustering has been proposed as a powerful computational tool to detect subsets of genes that exhibit consistent pattern over subsets of conditions. In this article, we review several recent patents in bicluster analysis, and in particular, highlight a recent patent from our group about a novel geometric-based biclustering method that handles the class of bicluster patterns with linear coherent variation across the row and/or column dimension. This class of bicluster patterns is of particular importance since it subsumes all constant, additive, and multiplicative bicluster patterns normally used in gene expression data analysis.

Many cellular processes exhibit cyclic behaviors. Hence, one important task in gene expression data analysis is to detect subset of genes that exhibit periodicity in their gene expression time series profiles. Unfortunately, gene expression time series profiles are usually of very short length, with very few periods, unevenly sampled, and are highly contaminated with noise. This makes detection of periodic profiles a very challenging problem. In this paper, we present several effective computational techniques developed recently in our research group for the reliable detection of short periodic gene expression time series profiles.

In recent years, there is a growing interest in using visual information for automatic lipreading (Kaynak, Zhi, Cheok, Sengupta, Jian, & Chung, 2004) and visual speaker authentication (Mok, Lau, Leung, Wang, & Yan, 2004). It has been shown that visual cues, such as lip shape and lip movement, would greatly improve the performance of these systems. Various techniques have been proposed in the past decades to extract speech/speaker relevant information from lip image sequences. One approach is to extract the lip contour from lip image sequences. This generally involves lip region segmentation and lip contour modeling (Liew, Leung, & Lau, 2002; Wang, Lau, Leung, & ALiew, 2004), and the performance of the visual speech recognition and visual speaker authentication systems depends much on the accuracy and efficiency of these two procedures. Lip region segmentation aims to label the pixels in the lip image into lip and non-lip. The accuracy and robustness of the lip segmentation process is of vital importance for subsequent lip extraction. However, large variations caused by different speakers, lighting condition, or make-ups make the task difficult. The low color contrast between lip and facial skin, and the presence of facial hair, further complicate the problem. Given a correctly segmented lip region, the lip extraction process then involves fitting a lip model to the lip region. A good lip model should be compact, that is, with a small number of parameters, and should adequately represent most valid lip shapes while rejecting most invalid shapes. As most lip extraction techniques involve iterative model fitting, the efficiency of the optimization process is another important issue.

With the rapid growth of Internet and multimedia systems, the use of visual information has increased enormously, such that indexing and retrieval techniques have become important. Historically, images are usually manually annotated with metadata such as captions or keywords (Chang & Hsu, 1992). Image retrieval is then performed by searching images with similar keywords. However, the keywords used may differ from one person to another. Also, many keywords can be used for describing the same image. Consequently, retrieval results are often inconsistent and unreliable. Due to these limitations, there is a growing interest in content-based image retrieval (CBIR). These techniques extract meaningful information or features from an image so that images can be classified and retrieved automatically based on their contents. Existing image retrieval systems such as QBIC and Virage extract the so-called low-level features such as color, texture and shape from an image in the spatial domain for indexing. Low-level features sometimes fail to represent high level semantic image features as they are subjective and depend greatly upon user preferences. To bridge the gap, a top-down retrieval approach involving high level knowledge can complement these low-level features. This articles deals with various aspects of CBIR. This includes bottom-up feature- based image retrieval in both the spatial and compressed domains, as well as top-down task-based image retrieval using prior knowledge.

Image compression aims to produce a new image representation that can be stored and transmitted efficiently. It is a core technology for multimedia processing and has played a key enabling role in many commercial products, such as digital camera and camcorders. It facilitates visual data transmission through the Internet, contributes to the advent of digital broadcast system, and makes possible the storage on VCD and DVD. Despite a continuing increase in capacity, efficient transmission and storage of images still present the utmost challenge in all these systems. Consequently, fast and efficient compression algorithms are in great demand. The basic principle for image compression is to remove any redundancy in image representation. For example, simple graphic images such as icons and line drawings can be represented more efficiently by considering differences among neighbor pixels, as the differences always have lower entropy value than the original images (Shannon, 1948). These kinds of techniques are often referred to as lossless compression. It tries to exploit statistical redundancy in an image so as to provide a concise representation in which the original image can be reconstructed perfectly. However, statistical compression techniques alone cannot provide high compression ratio. To improve image compressibility, lossy compression is often used so that visually important image features are preserved while some fine details are removed or not represented perfectly. This type of compression is often used for natural images where the loss of some details is generally unnoticeable to viewers. This articles deals with image compression. Specifi- cally, it is concern with compression of natural color images because they constitute the most important class of digital image. First, the basic principle and methodology of natural image compression is described. Then, several major natural image compression standards, namely JPEG, JPEG-LS, and JPEG 2000 are discussed.