We propose a video compression framework using conditional Generative Adversarial Networks (GANs). We rely on two encoders: one that deploys a standard video codec and another one which generates low-level soft edge maps. For decoding, we use a standard video decoder as well as a decoder that is trained using a conditional GAN. Recent "deep" approaches to video compression require multiple videos to pre-train generative networks that conduct interpolation. By contrast, our scheme trains a generative decoder that requires only a small number of key frames and edge maps taken from a single video, without any interpolation. Experiments on two video datasets demonstrate that the proposed GAN-based compression engine is a promising alternative to traditional video codec approaches that can achieve higher quality reconstructions for very low bitrates.

We consider the problem of depth estimation on digital stereo mammograms. Being able to elucidate 3D information from stereo mammograms is an important precursor to conducting 3D digital analysis of data from this promising new modality. The problem is generally much harder than the classic stereo matching problem on visible light images of the natural world, since nearly all of the 3D structural information of interest exists as complex network of multilayered, heavily occluded curvilinear structures. Toward addressing this difficult problem, we formulate a new stereo model that minimizes a global energy functional to densely estimate disparity on stereo mammogram images, by introducing a new singularity index as a constraint to obtain better estimates of disparity along critical curvilinear structures. Curvilinear structures, such as vasculature and spicules, are particularly salient structures in the breast, and being able to accurately position them in 3D is a valuable goal. Experiments on synthetic images with known ground truth and on real stereo mammograms highlight the advantages of the proposed stereo model over the canonical stereo model.

Biomedical informatics is the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health.1 ,2 Not only do biomedical informaticians study and develop theories, methods, and processes for the generation, manipulation, and sharing of biomedical data, but they also investigate how to model and reason on these data in order to effect beneficial change in the healthcare enterprise. In addition, an important aspect associated with developments in this field is the consideration of social and behavioral sciences in the design and evaluation of technical solutions. As a subfield of biomedical informatics, biomedical imaging informatics (BMII) encompasses all of the aforementioned aspects from the perspective of imaging. BMII has emerged as one of the fastest growing research areas in recent years given the evolution of techniques in molecular imaging, anatomical imaging, and functional imaging and advancements in imaging biomarker generation. Developments have also been accelerated by efforts to realize precision medicine,3 which necessitates a multiscale understanding of diseases that integrate insights in areas such as radiology, pathology, and genetics. This focus issue highlights the growing impact of BMII, demonstrating the increasing breadth of imaging modalities (eg, optical, molecular, in addition to traditional diagnostic modalities) and the diversity of specialties that depend on imaging information (eg, dermatology, pathology, surgery). Early efforts in BMII can be traced to the 1980s when the rise in radiological imaging techniques such as CT and MRI necessitated a digital, filmless approach to acquiring and interpreting images. The ability to acquire and distribute images electronically using picture archiving and communication systems (PACS) spawned a variety of applications aimed at improving radiological practice, research, and education. Imaging informatics efforts resulted in the development of specialized standardized … Correspondence to Dr William Hsu, Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA 90024, USA; willhsu{at}mii.ucla.edu

We propose a new steerable, multiscale ratio index for detecting impulse singularities in signals of arbitrary dimensionality. For example, it responds strongly to curvilinear masses (ridges) in images, but minimally to step discontinuities. The ratio index employs directional derivatives of gaussians, making it naturally steerable and scalable. Experiments on real images demonstrate the efficacy of the index for detecting multiscale curvilinear structures. A software version of the index can be downloaded from: http://live.ece.utexas.edu/research/SingularityIndex/SingularityIndex.zip.

We propose a new ratio index for the detection of impulse-like singularities in signals of arbitrary dimensionality. We show that the new singularity index responds strongly to singularities that are like impulses or smoothed impulses in cross section. For example, it responds strongly to curvilinear masses (ridges) in images, while responding minimally to edge-like singularities. The ratio index employs directional derivatives of gaussians, which makes the index naturally scalable.

High-throughput methods systematically measure the internal state of the entire cell, but powerful computational tools are needed to infer dynamics from their raw data. Therefore, we have developed a new computational method, Eigen-genomic System Dynamic-pattern Analysis (ESDA), which uses systems theory to infer dynamic parameters from a time series of gene expression measurements. As many genes are measured at a modest number of time points, estimation of the system matrix is underdetermined and traditional approaches for estimating dynamic parameters are ineffective; thus, ESDA uses the principle of dimensionality reduction to overcome the data imbalance. Since degradation rates are naturally confounded by self-regulation, our model estimates an effective degradation rate that is the difference between self-regulation and degradation. We demonstrate that ESDA is able to recover effective degradation rates with reasonable accuracy in simulation. We also apply ESDA to a budding yeast dataset, and find that effective degradation rates are normally slower than experimentally measured degradation rates. Our results suggest that either self-regulation is widespread in budding yeast and that self-promotion dominates self-inhibition, or that self-regulation may be rare and that experimental methods for measuring degradation rates based on transcription arrest may severely overestimate true degradation rates in healthy cells.

We present a new method called “snakules” for the annotation of spicules on mammography. Snakules employs parametric open-ended snakes that are deployed in a region around a suspect spiculated mass location that has been identified by a radiologist or a computer-aided detection (CADe) algorithm. The set of convergent snakules deform, grow and adapt to the true spicules in the image, by an attractive process of curve evolution and motion that optimizes the local matching energy. Our results from an initial observer study involving an experienced radiologist demonstrate the strong potential of the method as an image analysis technique to improve the specificity of CADe algorithms.

We have developed a novel, model-based active contour algorithm, termed "snakules", for the annotation of spicules on mammography. At each suspect spiculated mass location that has been identified by either a radiologist or a computer-aided detection (CADe) algorithm, we deploy snakules that are converging open-ended active contours also known as snakes. The set of convergent snakules have the ability to deform, grow and adapt to the true spicules in the image, by an attractive process of curve evolution and motion that optimizes the local matching energy. Starting from a natural set of automatically detected candidate points, snakules are deployed in the region around a suspect spiculated mass location. Statistics of prior physical measurements of spiculated masses on mammography are used in the process of detecting the set of candidate points. Observer studies with experienced radiologists to evaluate the performance of snakules demonstrate the potential of the algorithm as an image analysis technique to improve the specificity of CADe algorithms and as a CADe prompting tool.

We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a preprocessing image registration step, which is essential for other indices. Moreover, it is computationally less expensive.

Assessment of classifier performance is critical for fair comparison of methods, including considering alternative models or parameters during system design. The assessment must not only provide meaningful data on the classifier efficacy, but it must do so in a concise and clear manner. For two-class classification problems, receiver operating characteristic analysis provides a clear and concise assessment methodology for reporting performance and comparing competing systems. However, many other important biomedical questions cannot be posed as "two-class" classification tasks and more than two classes are often necessary. While several methods have been proposed for assessing the performance of classifiers for such multiclass problems, none has been widely accepted. The purpose of this paper is to critically review methods that have been proposed for assessing multiclass classifiers. A number of these methods provide a classifier performance index called the volume under surface (VUS). Empirical comparisons are carried out using 4 three-class case studies, in which three popular classification techniques are evaluated with these methods. Since the same classifier was assessed using multiple performance indexes, it is possible to gain insight into the relative strengths and weakness of the measures. We conclude that: 1) the method proposed by Scurfield provides the most detailed description of classifier performance and insight about the sources of error in a given classification task and 2) the methods proposed by He and Nakas also have great practical utility as they provide both the VUS and an estimate of the variance of the VUS. These estimates can be used to statistically compare two classification algorithms.

We present a systematic procedure for selecting facial fiducial points associated with diverse structural characteristics of a human face. We identify such characteristics from the existing literature on anthropometric facial proportions. We also present three dimensional (3D) face recognition algorithms, which employ Euclidean/geodesic distances between these anthropometric fiducial points as features along with linear discriminant analysis classifiers. Furthermore, we show that in our algorithms, when anthropometric distances are replaced by distances between arbitrary regularly spaced facial points, their performances decrease substantially. This demonstrates that incorporating domain specific knowledge about the structural diversity of human faces significantly improves the performance of 3D human face recognition algorithms.

We propose a novel 3D face recognition algorithm based on facial range image matching using the complex wavelet structural similarity metric (CW-SSIM) metric. Compared with many existing 3D surface matching methods, CW-SSIM is computationally efficient and is robust to small geometrical distortions. Using a data set that contains 360 3D face models of 12 subjects, we tested the performance of the proposed method and compared it with existing 3D surface matching based face recognition algorithms. Verification and identification performance of each algorithm was evaluated by means of the receiver operating characteristic curve and the cumulative match characteristic curve. Among the algorithms tested, the proposed algorithm based on the CW-SSIM resulted in the best overall performance with an equal error rate of 9.13% and a rank 1 recognition rate of 98.6%, significantly better than all the other algorithms. Besides the introduction of a novel approach for 3D face recognition, this is also the first attempt to expand the application scope of complex wavelet domain similarity measure to range image matching in general

We have invented a new class of linear filters for the detection of spiculated masses and architectural distortions in mammography. We call these Spiculation Filters. These filters are narrow band filters and form a new class of wavelet-type filter banks. In this paper, we show that unmodulated versions of these filters can be used to detect the central mass region of spiculated masses. We refer to these as toroidal gaussian filters. We also show that the physical properties of spiculated masses can be extracted from the responses of the toroidal gaussian filters without segmentation.

Inter- and intra-observer variability exists in any measurements made on medical images. There are two sources of variability. The first occurs when the observers identify and localize the object of interest, and the second happens when the observers make appropriate measurement on the object of interest. A number of statistical methods are available to quantify the degree of agreement between measurements made by different observers. However, little has been done to develop metrics for quantifying the variability in identifying and localizing the objects of interest prior to measurement. In this paper, we propose to use the complex wavelet structural similarity index (CW-SSIM) method to measure the variability in identifying and localizing structures on images. Performance comparisons using simulated images as well as real mammography images demonstrate the effectiveness and robustness of the CW-SSIM method.

Artificial neural networks (ANN) are frequently used in the development of Computer-Aided Diagnosis systems for breast cancer detection and diagnosis. One class of models uses descriptions of mammographic lesions encoded following the BI-RADS lexicon. Data sets that have been carefully curated to ensure completeness are generally used; however, in routine practice, some information is typically missing in clinical databases. The impact of missing data on the performance of a feedforward, back-propagation ANN, as measured by the area under the Receiver Operating Characteristic curve, was found to be much higher when data were missing from the testing set than when data were missing from the training set. This empirical study highlights the need for additional research on developing robust clinical decision support systems for realistic environments in which key information may be unknown or inaccessible.