Zhongmin Liu

Cascaded differential and wavelet compression of chromosome images

This paper proposes a new method for chromosome image compression based on an important characteristic of these images: the regions of interest (ROIs) to cytogeneticists for evaluation and diagnosis are well determined and segmented. Such information is utilized to advantage in our compression algorithm, which combines lossless compression of chromosome ROIs with lossy-to-lossless coding of the remaining image parts. This is accomplished by first performing a differential operation on chromosome ROIs for decorrelation, followed by critically sampled integer wavelet transforms on these regions and the remaining image parts. The well-known set partitioning in hierarchical trees (SPIHT) (Said and Perlman, 1996) algorithm is modified to generate separate embedded bit streams for both chromosome ROIs and the rest of the image that allow continuous lossy-to-lossless compression of both (although lossless compression of the former is commonly used in practice). Experiments on two sets of sample chromosome spread and karyotype images indicate that the proposed approach significantly outperforms current compression techniques used in commercial karyotyping systems and JPEG-2000 compression, which does not provide the desirable support for lossless compression of arbitrary ROIs.

Lossy-to-lossless ROI coding of chromosome images using modified SPIHT and EBCOT

This paper proposes a lossy-to-lossless region of interest (ROI) compression scheme based on set partitioning in hierarchical trees (SPIHT) and embedded block coding with optimized truncation (EBCOT) (EBCOT is the base of the JPEG-2000 standard). However, SPIHT does not support ROI coding and JPEG-2000 does not allow lossy-to-lossless ROI compression. For our application, we modify the original SPIHT and EBCOT algorithms for lossy-to-lossless ROI coding of both the foreground and the background of chromosome images. Experiments show that our implementations offer better compression performance with exact ROI support than other image coders.

Microarray BASICA: background adjustment, segmentation, image compression and analysis of microarray images

This paper presents Microarray BASICA: an integrated image processing tool for background adjustment, segmentation, image compression and analysis of microarray images. BASICA uses the fast Mann-Whitney test-based algorithm introduced in J. Hua et al., (2002) to segment microarray images, and post-processing to eliminate the segmentation irregularities. The segmentation results, along with the foreground and background intensities obtained with background adjustment, are then used for the independent compression of foreground and background. We introduce a new distortion measure for microarray image compression and devise a coding scheme by modifying the object-based embedded block coding with optimized truncation (object-based EBCOT) algorithm J. Hua et al., (2002) to achieve optimal rate-distortion performance in lossy coding while still maintaining outstanding lossless compression performance. Experimental results show that BASICA can extract sufficiently accurate genetic information at bitrates as low as 43bpp.

The effect of image enhancement on biomedical pattern recognition

Image enhancement has been an area of active research for decades. Most studies were aimed at improving the quality of image display for better visualization. Yet few studies have been conducted to investigate the impact of image enhancement on biomedical pattern recognition. In this paper, we examine quantitatively the effect of image enhancement on the performance of biomedical pattern recognition. We apply the wavelet-based image enhancement technique developed in our earlier work [Y. Wang et al., Proc. ICASSP 2001, Salt Lake City, May, 2001], to a well-known biomedical pattern recognition problem: chromosome classification. Experiments were conducted on a test set of chromosome images before and after the enhancement, using the same feature measurement and classifier methods. The test results show that our image enhancement method substantially reduces the error rate of chromosome classification. We learn from this study that proper image enhancement leads to significantly improved recognition accuracy, and the quantification of performance improvement may be used as an objective measure of success for evaluating various image enhancement techniques.

Joint UEP and layered source coding with application to transmission of JPEG-2000 coded images

This paper presents a joint source-channel coding framework based on layered source coding and Reed-Solomon channel coding for unequal error protection. An iterative procedure is described to search for the best source coding rate and the optimal UEP of layered bitstreams. We apply our JSCC technique to transmission of JPEG-2000 coded images over binary symmetric channels. Compared to results reported in the literature, our UEP based approach gives better results while having lower complexity.

Efficient rate allocation for progressive image transmission via unequal error protection over finite-state Markov channels

This paper proposes a unified framework for addressing progressive image transmission over noisy channels based on the finite-state Markov channel (FSMC) model. FSMC models are simple yet general enough to model binary symmetric, Gilbert-Elliott, and fading channels. They allow error sequence analysis that facilitates quantifying the statistical characteristics of the embedded bitstreams transmitted over FSMC in closed form. Using a concatenation of rate-compatible puncturing convolutional code and cyclic redundancy check code for error protection, we use a concatenation of rate-compatible punctured convolutional code and cyclic redundancy check code for error protection, which results in an unequal error protection (UEP) system, and find (sub-)optimal rate allocation solutions for our setup. By mapping fading channels to FSMCs, the JSCC problem is thus solved without the burden of simulations using an image-dependent lookup table. Fast algorithms are proposed to search for the optimal UEP. Experiments on embedded image bitstreams over FSMCs confirm our analytical results.

On optimal subspaces for appearance-based object recognition

On the subject of optimal subspaces for appearance-based object recognition, it is generally believed that algorithms based on LDA (linear discriminant analysis) are superior to those based on PCA (principal components analysis), provided that relatively large training data sets are available. In this paper, we show that while this is generally true for classification with the nearest-neighbor classifier, it is not always the case with a maximum-likelihood classifier. We support our claim by presenting both intuitively plausible arguments and actual results on a large data set of human chromosomes. Our conjecture is that perhaps only when the underlying object classes are linearly separable would LDA be truly superior to other known subspaces of equal dimensionality.

Optimal error protection for progressive image transmission over finite-state Markov channels

This paper presents a unified framework for addressing progressive image transmission over both memoryless and fading channels based on finite-state Markov channels (FSMC) models. The main advantage of using FSMC models is that they allow analytical derivation of optimal joint source-channel coding solutions in the form of unequal error protection without the burden of simulating wireless channels. Our analyses and experiments confirm that 1) FSMC models work for fading channels and 2) PSNR results obtained with FSMC models compare favorably with those published in the literature.

Fast filter bank implementation of image interpolation at any scale

Image interpolation has been extensively studied. In this paper, we present a fast filter bank algorithm for interpolating images at any scales. The proposed algorithm is a generalization of [1] and [2], which handles only either integer or dyadic scale magnification of images. The details of the algorithm and implementation are described in the paper. In particular, we show that several image interpolation algorithms are the special cases of our proposed algorithm. The comparison with other interpolating bases are also discussed. The proposed algorithm is of practical use for enlargement in display and printing applications.

Human chromosome image compression using cascaded differential and wavelet coding

Chromosome analysis is an important procedure in clinical and cancer cytogenetics. Efficient image compression techniques are highly desired to accommodate the rapid growth in the use of digital media for archiving, storage and communication of chromosome spread images. In this paper, we propose a new method based on an important characteristic of these images: the regions of interest to cytogeneticists for evaluation and diagnosis are well determined and segmented. Such information is utilized to advantage in our compression algorithm, which combines lossless coding of chromosome regions with lossy-to-lossless coding of the remaining image parts. This is accomplished by first performing a differential operation on chromosome regions for decorrelation, followed by critically sampled integer wavelet transforms on these regions and the remaining image parts. A modified set partitioning in hierarchical trees (SPIHT) algorithm is then used to generate separate embedded bitstreams that allow continuous lossy-to-lossless compression of both chromosome regions and the rest of the image (although lossless coding of the former is commonly used in practice). Experiments on sample chromosome spread images indicate that the proposed approach significantly outperforms several reference compression schemes and the techniques currently employed in commercial systems.