MOTIVATION: The translational landscape of diverse cellular systems remains largely uncharacterized. A detailed understanding of the control of gene expression at the level of messenger RNA translation is vital to elucidating a systems-level view of complex molecular programs in the cell. Establishing the degree to which such post-transcriptional regulation can mediate specific phenotypes is similarly critical to elucidating the molecular pathogenesis of diseases such as cancer. Recently, methods for massively parallel sequencing of ribosome-bound fragments of messenger RNA have begun to uncover genome-wide translational control at codon resolution. Despite its promise for deeply characterizing mammalian proteomes, few analytical methods exist for the comprehensive analysis of this paired RNA and ribosome data. RESULTS: We describe the Babel framework, an analytical methodology for assessing the significance of changes in translational regulation within cells and between conditions. This approach facilitates the analysis of translation genome-wide while allowing statistically principled gene-level inference. Babel is based on an errors-in-variables regression model that uses the negative binomial distribution and draws inference using a parametric bootstrap approach. We demonstrate the operating characteristics of Babel on simulated data and use its gene-level inference to extend prior analyses significantly, discovering new translationally regulated modules under mammalian target of rapamycin (mTOR) pathway signaling control.

MOTIVATION: High-throughput techniques facilitate the simultaneous measurement of DNA copy number at hundreds of thousands of sites on a genome. Older techniques allow measurement only of total copy number, the sum of the copy number contributions from the two parental chromosomes. Newer single nucleotide polymorphism (SNP) techniques can in addition enable quantifying parent-specific copy number (PSCN). The raw data from such experiments are two-dimensional, but are unphased. Consequently, inference based on them necessitates development of new analytic methods. METHODS: We have adapted and enhanced the circular binary segmentation (CBS) algorithm for this purpose with focus on paired test and reference samples. The essence of paired parent-specific CBS (Paired PSCBS) is to utilize the original CBS algorithm to identify regions of equal total copy number and then to further segment these regions where there have been changes in PSCN. For the final set of regions, calls are made of equal parental copy number and loss of heterozygosity (LOH). PSCN estimates are computed both before and after calling. RESULTS: The methodology is evaluated by simulation and on glioblastoma data. In the simulation, PSCBS compares favorably to established methods. On the glioblastoma data, PSCBS identifies interesting genomic regions, such as copy-neutral LOH. AVAILABILITY: The Paired PSCBS method is implemented in an open-source R package named PSCBS, available on CRAN (http://cran.r-project.org/).

Nonparametric neighborhood methods for learning entail estimation of class conditional probabilities based on relative frequencies of samples that are "near-neighbors" of a test point. We propose and explore the behavior of a learning algorithm that uses linear interpolation and the principle of maximum entropy (LIME). We consider some theoretical properties of the LIME algorithm: LIME weights have exponential form; the estimates are consistent; and the estimates are robust to additive noise. In relation to bias reduction, we show that near-neighbors contain a test point in their convex hull asymptotically. The common linear interpolation solution used for regression on grids or look-up-tables is shown to solve a related maximum entropy problem. LIME simulation results support use of the method, and performance on a pipeline integrity classification problem demonstrates that the proposed algorithm has practical value.

This paper treats a multiresolution hidden Markov model for classifying images. Each image is represented by feature vectors at several resolutions, which are statistically dependent as modeled by the underlying state process, a multiscale Markov mesh. Unknowns in the model are estimated by maximum likelihood, in particular by employing the expectation-maximization algorithm. An image is classified by finding the optimal set of states with maximum a posteriori probability. States are then mapped into classes. The multiresolution model enables multiscale information about context to be incorporated into classification. Suboptimal algorithms based on the model provide progressive classification that is much faster than the algorithm based on single-resolution hidden Markov models.

We present an algorithm to achieve good compression and classification for images using vector quantization and a two dimensional hidden Markov model. The feature vectors of image blocks are assumed to be generated by a two dimensional hidden Markov model. We first estimate the parameters of the model, then design a vector quantizer to minimize a weighted sum of compression distortion and classification risk, the latter being defined as the negative of the maximum log likelihood of states and feature vectors. The algorithm is tested on both synthetic data and real image data. The extension to joint progressive compression and classification is discussed.

We consider the problem of joint quantization and classification for the example of a simple Gaussian mixture used by Kohonen (1988) to demonstrate the performance of his "learning vector quantization" (LVQ). Implicit in the problem is the issue of estimating the underlying densities, which is accomplished by CART/sup TM/ and by an inverse halftoning method.

The substitution of digital representations for analog images provides access to methods for digital storage and transmission and enables the use of a variety of digital image processing techniques, including enhancement and computer assisted screening and diagnosis. Lossy compression can further improve the efficiency of transmission and storage and can facilitate subsequent image processing. Both digitization (or digital acquisition) and lossy compression alter an image from its traditional form, and hence it becomes important that any such alteration be shown to improve or at least not damage the utility of the image in a screening or diagnostic application. One approach to demonstrating in a quantifiable manner that a specific image mode is at least equal to another is by clinical experiment simulating ordinary practice and suitable statistical analysis. In this paper we describe a general protocol for performing such a verification and present preliminary results of a specific experiment designed to show that 12 bpp digital mammograms compressed in a lossy fashion to 0.015 bpp using an embedded wavelet coding scheme result in no significant differences from the analog or digital originals. Die Ersetzung analoger Bilder durch digitale Darstellungen erlaubt eine digitale Speicherung und Übertragung sowie den Einsatz einer Vielzahl von Methoden der digitalen Bildverarbeitung, z.B. zur Verbesserung der Bildqualität und zum computerunterstützten Screening bzw. zur computerunterstützten Diagnose. Eine verlustbehaftete Kompression kann die Effizienz der Übertragung oder Speicherung weiter steigern und eine nachfolgende Bildverarbeitung erleichtern. Sowohl die Digitalisierung (oder digitale Aufnahme) als auch die verlustbehaftete Kompression ändern ein Bild bezüglich seiner ursprünglichen Form. Deswegen ist es wichtig, zu zeigen daβ eine solche Veränderung die Nützlichkeit des Bildes bei Screening- oder diagnostischen Anwendungen steigert oder wenigstens nicht beeinträchtigt. Eine Möglichkeit, auf quantifizierbare Weise zu zeigen, daβ eine bestimmte Bilddarstellung einer anderen zumindest äquivalent ist, ist ein die gewöhnliche Praxis simulierendes klinisches Experiment und eine geeignete statistische Analyse. In diesem Artikel beschreiben wir ein allgemeines Protokoll für die Durchführung einer solchen Verifikation. Wir präsentieren weiters vorläufige Resultate eines spezifischen Experiments, welches zeigt, daβ die verlustbehaftete Kompression digitaler Mammogramme von 12 bpp auf 0.15 bpp mittels einer eingebetteten Wavelet-Codierung zu keinen signifikanten Unterschieden von den analogen oder digitalen Originalen führt. La substitution d'images analogiques par des représentations numériques donne accès à des méthodes de stockage et de transmission numériques, et permet l'utilisation d'une grande variété de techniques de traitement d'images, incluant le rehaussement, les tests de dépistage assisté ordinateur et le diagnostic. La compression avec pertes peut encore améliorer l'efficacité de la transmission et du stockage, et peut faciliter le traitement ultérieur des images. La numérisation et la compression avec pertes altérant toutes deux une image par rapport à sa forme traditionnelle, il devient important de montrer qu'une telle altération améliore, ou du moins ne réduit pas, l'utilité de l'image dans un screening ou une application de diagnostic. Une approche pour démontrer d'une manière quantifiable qu'un mode d'image spécifique est au moins égal à un autre est l'expérimentation clinique simulant la pratique ordinaire jointe à une analyse statistique adaptée. Dans cet article, nous décrivons un protocole général pour effectuer une telle vérification et présentons les résultats préliminaires d'une expérience faite pour montrer que des mamogrammes numérisés à 12 bpp et comprimés avec pertes à 0.15 bpp à l'aide d'une technique de codage par ondelettes incluses ne présentent pas de différences significatives par rapport aux versions originales analogique ou numérique.

Prediction can play an important role in image compression. Better rate-distortion tradeoffs can be achieved by coding the residuals from predictive schemes rather than the direct pixel values. The price is only a modest increase in complexity. A variety of linear and nonlinear predictors have been used successfully in applications of predictive vector quantization. It makes sense that the better the prediction, the better the resulting compression. One method by which to improve predictive accuracy is to reduce the variability of the predictions. We here apply ridge regression in order to obtain prediction coefficients for use in a predictive vector quantizer as an alternative to standard Wiener-Hopf techniques.

Multimedia applications such as educational videos and color facsimile contain images that are rich in both textual and continuous tone data. Because these two types of data have different properties, segmentation of the images into text and continuous tone data can improve compression by allowing different compression parameters or even algorithms to be employed on the different types. We propose and compare algorithms that use classification trees (CLTR) or tree-structured vector quantization (TSVQ) for block-based classification in mixed-mode images. We also examine different types of features that can be used in these classifiers. The results show that using linear transform features with either the CLTR or TSVQ can be effective for accurate text classification. In addition, the results indicate that combining these classifiers with another TSVQ that is designed simultaneously to minimize both compression and classification error can provide better classification than does either system alone.

Classification and compression play important roles in communicating digital information. Their combination is useful in many applications, including the detection of abnormalities in compressed medical images. In view of the similarities of compression and low-level classification, it is not surprising that there are many similar methods for their design. Because some of these methods are useful for designing vector quantizers, it seems natural that vector quantization (VQ) is explored for the combined goal. We investigate several VQ-based algorithms that seek to minimize both the distortion of compressed images and errors in classifying their pixel blocks. These algorithms are investigated with both full search and tree-structured codes. We emphasize a nonparametric technique that minimizes both error measures simultaneously by incorporating a Bayes risk component into the distortion measure used for the design and encoding. We introduce a tree-structured posterior estimator to produce the class posterior probabilities required for the Bayes risk computation in this design. For two different image sources, we demonstrate that this system provides superior classification while maintaining compression close or superior to that of several other VQ-based designs, including Kohonen's (1992) "learning vector quantizer" and a sequential quantizer/classifier design.

Tree-structured vector quantizers (TSVQ) provide a computationally efficient, variable-rate method of compressing vector-valued data. In applications, the problem of designing a TSVQ from empirical training data is critical. Greedy growing algorithms are a common and effective approach to the design problem. They are recursive procedures that produce a TSVQ one node at a time by optimizing a simple splitting criterion at each step. While unsupervised greedy growing algorithms are well-understood from an experimental point of view, there has been little theory to support their use, or to examine their behavior on large training sets. The authors present a rigorous analysis of a greedy growing algorithm proposed by Riskin (1990), Riskin and Gray (1991), and Balakrishnan (1991). The first part of the paper is a description of the algorithm and an examination of its asymptotic behavior as it applies to a fixed, absolutely continuous distribution. The second part of the paper establishes the structural consistency of the algorithm with respect to a convergent sequence of distributions. As an application, the authors obtain results concerning the large-sample empirical behavior of the algorithm when it is applied to stationary ergodic training vectors.

A Bayes risk weighted vector quantizer (Bayes VQ) combines compression and low-level classification of images by incorporating a Bayes risk component into the distortion measure used to design the code. The class posterior probabilities required for the Bayes risk computation can be estimated based on a labeled training sequence. We introduce two new methods for estimating these posteriors. In particular, two types of tree-structured estimators are constructed by applying the classification and regression tree algorithm CART to eight features of the training sequence. We apply the resulting Bayes VQ systems to aerial photographs where the goal is to compress the images and classify man-made and natural regions. These systems provide classification superior to that of previous work with Bayes VQ while maintaining similar compression performance. The systems also provide moderate to substantial improvement in classification with only a small loss in compression to performance obtained with a modified version of Kohonen's (1988) "learning vector quantizer" and with an independent design of quantizer and classifier.

Image quality and utility become crucial issues for engineers, scientists, patients, regulators, administrators, insurance companies, and lawyers whenever there are changes in the technology by which medical images are produced. Examples of such changes include analog-to-digital conversion, lossy compression for efficient transmission and storage, image enhancement, and computer-aided methodology for diagnosis that affects the appearances of images. This paper is a summary of some principles for designing protocols for clinical experiments to quantify the relative qualities and utilities of different images, here analog, digital, and lossy compressed digital mammograms. A talk supplemented this paper with a status report on the specific experiment described which is scheduled to be conducted during summer 1995.

The authors use predictive pruned tree-structured vector quantization for the compression of medical images. Their goal is to obtain a high compression ratio without impairing the image quality, at least so far as diagnostic purposes are concerned. The authors use a priori knowledge of the class of images to be encoded to help them segment the images and thereby to reserve bits for diagnostically relevant areas. Moreover, the authors improve the quality of prediction and encoding in two additional ways: by increasing the memory of the predictor itself and by using ridge regression for prediction. The improved encoding scheme was tested via computer simulations on a set of mediastinal CT scans; results are compared with those obtained using a more conventional scheme proposed recently in the literature. There were remarkable improvements in both the prediction accuracy and the encoding quality, above and beyond what comes from the segmentation. Test images were encoded at 0.5 bit per pixel and less without any visible degradation for the diagnostically relevant region.

The authors investigate a method that combines compression and low-level classification of images by designing codes that contain implicit information regarding classification. The design consists of a tree-structured vector quantizer (TSVQ) that incorporates a Bayes risk term into the distortion measure used in the quantizer design algorithm in order to permit a tradeoff of mean squared error and classification error. Once designed, the quantizer can operate to minimize the Bayes risk weighted distortion measure by incorporating the posterior probabilities into the encoding process. A completely nonparametric design algorithm is constructed by estimating these posterior distributions using a TSVQ that incorporates the classification error into the splitting criterion. This approach is used to analyze simulated data and to identify tumors in CT lung images. Comparisons are made with other vector quantizer based classifiers, including Kohonen's "learning vector quantizer." For the examples considered, their method provided a classification that was superior to the other methods while simultaneously providing close to or superior compression performance.>

We investigated the effects of lossy image compression on measurement accuracy in magnetic resonance images. Thirty chest scans were compressed to five different levels using predictive pruned tree-structured vector quantization (predictive PTSVQ). Three radiologists measured the diameters of the four principal blood vessels on each image. Errors were analyzed relative to both an independent standard and personal performance on uncompressed images. Data were compared with both t and Wilcoxon tests. We conclude that for the purpose of measuring blood vessels in the chest, there is no significant difference in measurement accuracy when images are compressed up to 16:1 with predictive PTSVQ.>

Compressing a digital image can facilitate its transmission, storage, and processing. As radiology departments become increasingly digital, the quantities of their imaging data are forcing consideration of compression in picture archiving and communication systems (PACS) and evolving teleradiology systems. Significant compression is achievable only by lossy algorithms, which do not permit the exact recovery of the original image. This loss of information renders compression and other image processing algorithms controversial because of the potential loss of quality and consequent problems regarding liability, but the technology must be considered because the alternative is delay, damage, and loss in the communication and recall of the images. How does one decide if an image is good enough for a specific application, such as diagnosis, recall, archival, or educational use? The authors describe three approaches to the measurement of medical image quality: signal-to-noise ratio (SNR), subjective rating, and diagnostic accuracy. They compare and contrast these measures in a particular application, consider in some depth recently developed methods for determining diagnostic accuracy of lossy compressed medical images and examine how good the easily obtainable distortion measures like SNR are at predicting the more expensive subjective and diagnostic ratings. The examples are of medical images compressed using predictive pruned tree-structured vector quantization, but the methods can be used for any digital image processing that produces images different from the original for evaluation.>

The authors apply a lossy compression algorithm to medical images, and quantify the quality of the images by the diagnostic performance of radiologists, as well as by traditional signal-to-noise ratios and subjective ratings. The authors' study is unlike previous studies of the effects of lossy compression in that they consider nonbinary detection tasks, simulate actual diagnostic practice instead of using paired tests or confidence rankings, use statistical methods that are more appropriate for nonbinary clinical data than are the popular receiver operating characteristic curves, and use low-complexity predictive tree-structured vector quantization for compression rather than DCT-based transform codes combined with entropy coding. The authors' diagnostic tasks are the identification of nodules (tumors) in the lungs and lymphadenopathy in the mediastinum from computerized tomography (CT) chest scans. Radiologists read both uncompressed and lossy compressed versions of images. For the image modality, compression algorithm, and diagnostic tasks the authors consider, the original 12 bit per pixel (bpp) CT image can be compressed to between 1 bpp and 2 bpp with no significant changes in diagnostic accuracy. The techniques presented here for evaluating image quality do not depend on the specific compression algorithm and are useful new methods for evaluating the benefits of any lossy image processing technique.