Kenneth R. Castleman

Chromosome image enhancement using multiscale differential operators

Chromosome banding patterns are very important features for karyotyping, based on which cytogenetic diagnosis procedures are conducted. Due to cell culture, staining, and imaging conditions, image enhancement is a desirable preprocessing step before performing chromosome classification. In this paper, we apply a family of differential wavelet transforms (Wang and Lee, 1998), (Wang, 1999) for this purpose. The proposed differential filters facilitate the extraction of multiscale geometric features of chromosome images. Moreover, desirable fast computation can be realized. We study the behavior of both banding edge pattern and noise in the wavelet transform domain. Based on the fact that image geometrical features like edges are correlated across different scales in the wavelet representation, a multiscale point-wise product (MPP) is used to characterize the correlation of the image features in the scale-space. A novel algorithm is proposed for the enhancement of banding patterns in a chromosome image. In order to compare objectively the performance of the proposed algorithm against several existing image-enhancement techniques, a quantitative criteria, the contrast improvement ratio (CIR), has been adopted to evaluate the enhancement results. The experimental results indicate that the proposed method consistently outperforms existing techniques in terms of the CIR measure, as well as in visual effect. The effect of enhancement on cytogenetic diagnosis is further investigated by classification tests conducted prior to and following the chromosome image enhancement. In comparison with conventional techniques, the proposed method leads to better classification results, thereby benefiting the subsequent cytogenetic diagnosis.

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.

Subspace-based prototyping and classification of chromosome images

Chromosomes are essential genomic information carriers. Chromosome classification constitutes an important part of routine clinical and cancer cytogenetics analysis. Cytogeneticists perform visual interpretation of banded chromosome images according to the diagrammatic models of various chromosome types known as the ideograms, which mimic artists depiction of the chromosomes. In this paper, we present a subspace-based approach for automated prototyping and classification of chromosome images. We show that 1) prototype chromosome images can be quantitatively synthesized from a subspace to objectively represent the chromosome images of a given type or population, and 2) the transformation coefficients (or projected coordinate values of sample chromosomes) in the subspace can be utilized as the extracted feature measurements for classification purposes. We examine in particular the formation of three well-known subspaces, namely the ones derived from principal component analysis (PCA), Fishers linear discriminant analysis, and the discrete cosine transform (DCT). These subspaces are implemented and evaluated for prototyping two-dimensional (2-D) images and for classification of both 2-D images and one-dimensional profiles of chromosomes. Experimental results show that previously unseen prototype chromosome images of high visual quality can be synthesized using the proposed subspace-based method, and that PCA and the DCT significantly outperform the well-known benchmark technique of weighted density distribution functions in classifying 2-D chromosome images.

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.

Image enhancement using multiscale differential operators

Differential operators have been widely used for multiscale geometric descriptions of images. Efficient computation of these differential operators can be obtained by taking advantage of the spline techniques. We make use of a special class of these operators for image enhancement, with a particular application to chromosome image enhancement. These operators constitute a translation invariant wavelet transform well suited for the structural description of chromosome geometry. Based on the fact that the geometrical features like edges are correlated between different scales in the representation, a novel algorithm is designed to enhance the salient features of the image. Comparisons of this algorithm with other approaches are presented.

3-D Face Recognition Based on Warped Example Faces

In this paper, we describe a novel 3-D face recognition scheme for 3-D face recognition that can automatically identify faces from range images, and is insensitive to holes, facial expression, and hair. In our scheme, a number of carefully selected range images constitute a set of example faces, and another range image is chosen as a ldquogeneric face.rdquo The generic face is then warped to match each of the example faces in the least mean square sense. Each such warp is specified by a vector of displacement values. In feature extraction operation, when a target face image comes in, the generic face is warped to match it. The geometric transformation used in the warping is a linear combination of the example face warping vectors. The coefficients in the linear combination are adjusted to minimize the root mean square error. After the matching process is complete, the coefficients of the composite warp are used as features and passed to a Mahalanobis-distance-based classifier for face recognition. Our technique is tested on a data set containing more than 600 range images. Experimental results in the access-control scenario show the effectiveness of the extracted features.

Image enhancement using multiscale oriented wavelets

We describe a novel method of enhancing image geometric features using the oriented wavelets introduced in our earlier work (see Yu-Ping Wang, IEEE Trans. Image Proc., vol.8, no.12, p.1757-71, 1999). The poor directional selectivity of the conventional 2D wavelet transform has been circumvented by using this class of oriented wavelets. By taking advantage of directional wavelet decomposition, both the directional and scale correlation information are utilized for enhancing salient structures in images. To remove phase dependence, a pair of quadrature filters are used. The proposed algorithm has been applied to chromosome image enhancement. Comparisons of this algorithm with other approaches are presented.

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.