Ngai-Fong Law

Missing value imputation for gene expression data: computational techniques to recover missing data from available information

Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.

Image segmentation based on adaptive cluster prototype estimation

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.

BiVisu: software tool for bicluster detection and visualization

UNLABELLEDnBiVisu is an open-source software tool for detecting and visualizing biclusters embedded in a gene expression matrix. Through the use of appropriate coherence relations, BiVisu can detect constant, constant-row, constant-column, additive-related as well as multiplicative-related biclusters. The biclustering results are then visualized under a 2D setting for easy inspection. In particular, parallel coordinate (PC) plots for each bicluster are displayed, from which objective and subjective cluster quality evaluation can be performed.nnnAVAILABILITYnBiVisu has been developed in Matlab and is available at http://www.eie.polyu.edu.hk/~nflaw/Biclustering/.

POCS-based blocking artifacts suppression using a smoothness constraint set with explicit region modeling

It is well known that low bit rate block-based discrete cosine transform coded image exhibits visually annoying coding artifacts. In this paper, we proposed a projection onto convex sets (PCOS)-based deblocking algorithm using a novel region smoothness constraint set for graphic images containing objects with smooth regions. The smoothness constraint set is obtained by an explicit modeling of smooth regions in the image using a spatially adaptive thin-plate spline. In contrast to most deblocking algorithms which enforce smoothness just around the 8/spl times/8 block boundaries, our algorithm enforces smoothness in regions which could possibly span several blocks. We showed that convergence of our algorithm could be reached within one iteration. The performance of the proposed algorithm is evaluated visually and quantitatively in term of peak signal-to-noise ratios and the mean squared difference of slope metric, which measures the impact of the blocking effects, for several graphic images. The results show that our algorithm can effectively suppress blockiness in smooth regions while still preserving the sharpness of object edges.

A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT

Due to its excellent rate-distortion performance, set partitioning in hierarchical trees (SPIHT) has become the state-of-the-art algorithm for image compression. However, the algorithm does not fully provide the desired features of progressive transmission, spatial scalability and optimal visual quality, at very low bit rate coding. Furthermore, the use of three linked lists for recording the coordinates of wavelet coefficients and tree sets during the coding process becomes the bottleneck of a fast implementation of the SPIHT. In this paper, we propose a listless modified SPIHT (LMSPIHT) approach, which is a fast and low memory image coding algorithm based on the lifting wavelet transform. The LMSPIHT jointly considers the advantages of progressive transmission, spatial scalability, and incorporates human visual system (HVS) characteristics in the coding scheme; thus it outperforms the traditional SPIHT algorithm at low bit rate coding. Compared with the SPIHT algorithm, LMSPIHT provides a better compression performance and a superior perceptual performance with low coding complexity. The compression efficiency of LMSPIHT comes from three aspects. The lifting scheme lowers the number of arithmetic operations of the wavelet transform. Moreover, a significance reordering of the modified SPIHT ensures that it codes more significant information belonging to the lower frequency bands earlier in the bit stream than that of the SPIHT to better exploit the energy compaction of the wavelet coefficients. HVS characteristics are employed to improve the perceptual quality of the compressed image by placing more coding artifacts in the less visually significant regions of the image. Finally, a listless implementation structure further reduces the amount of memory and improves the speed of compression by more than 51% for a 512x512 image, as compared with that of the SPIHT algorithm.

Multiscale directional filter bank with applications to structured and random texture retrieval

In this paper, multiscale directional filter bank (MDFB) is investigated for texture characterization and retrieval. First, the problem of aliasing in decimated bandpass images on directional decomposition is addressed. MDFB is then designed to suppress the aliasing effect as well as to minimize the reduction in frequency resolution. Second, an entropy-based measure on energy signatures is proposed to classify structured and random textures. With the use of this measure for texture pre-classification, an optimized retrieval performance can be achieved by selecting the MDFB-based method for retrieving structured textures and a statistical or model-based method for retrieving random textures. In addition, a feature reduction scheme and a rotation-invariant conversion method are developed. The former is developed so as to find the most representative features while the latter is developed to provide a set of rotation-invariant features for texture characterization. Experimental works confirm that they are effective for texture retrieval.

Effective bi-directional people flow counting for real time surveillance system

In this paper, an object based bi-directional counting system is proposed, which comprises of an advanced object detection and tracking algorithm to count the people flow in the monitoring scene. The result of which shows that the approach can provide about 90% accuracy for bi-directional people counting with an angle of 45° to the scene.

Statistical power of Fisher test for the detection of short periodic gene expression profiles

Many cellular processes exhibit periodic behaviors. Hence, one of the important tasks in gene expression data analysis is to detect subset of genes that exhibit cyclicity or periodicity in their gene expression time series profiles. Unfortunately, gene expression time series profiles are usually of very short length, with very few periods, irregularly sampled and are highly contaminated with noise. This makes the detection of periodic profiles a very challenging problem. Recently, a hypothesis testing method based on the Fisher g-statistic with correction for multiple testing has been proposed to detect periodic gene expression profiles. However, it was observed that the test is not reliable if the signal length is too short. In this paper, we performed extensive simulation study to investigate the statistical power of the test as a function of noise distribution, signal length, SNR, and the false discovery rate (FDR). We have found that the number of periodic profiles can be severely underestimated for short length signal. The findings indicate that caution needs to be exercised when interpreting the test result for very short length signals.

Cross chromosomal similarity for DNA sequence compression

Current DNA compression algorithms work by finding similar repeated regions within the DNA sequence and then encoding these regions together to achieve compression. Our study on chromosome sequence similarity reveals that the length of similar repeated regions within one chromosome is about 4.5% of the total sequence length. The compression gain is often not high because of these short lengths. It is well known that similarity exist among different regions of chromosome sequences. This implies that similar repeated sequences are found among different regions of chromosome sequences. Here, we study cross-chromosomal similarity for DNA sequence compression. The length and location of similar repeated regions among the sixteen chromosomes of S. cerevisiae are studied. It is found that the average percentage of similar subsequences found between two chromosome sequences is about 10% in which 8% comes from cross-chromosomal prediction and 2% from self-chromosomal prediction. The percentage of similar subsquences is about 18% in which only 1.2% comes from self-chromosomal prediction while the rest is from cross-chromosomal prediction among the 16 chromosomes studied. This suggests the importance of cross-chromosomal similarities in addition to self-chromosomal similarities in DNA sequence compression. An additional 23% of storage space could be reduced on average using self-chromosomal and cross-chromosomal predictions in compressing the 16 chromosomes of S. cerevisiae.

Brain symmetry plane detection based on fractal analysis.

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.