Showing papers by "Alan C. Bovik published in 2012"

No-Reference Image Quality Assessment in the Spatial Domain

Abstract: We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of “naturalness” in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.

Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain

Abstract: We develop an efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.

RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment

Abstract: We study the problem of automatic “reduced-reference” image quality assessment (QA) algorithms from the point of view of image information change. Such changes are measured between the reference- and natural-image approximations of the distorted image. Algorithms that measure differences between the entropies of wavelet coefficients of reference and distorted images, as perceived by humans, are designed. The algorithms differ in the data on which the entropy difference is calculated and on the amount of information from the reference that is required for quality computation, ranging from almost full information to almost no information from the reference. A special case of these is algorithms that require just a single number from the reference for QA. The algorithms are shown to correlate very well with subjective quality scores, as demonstrated on the Laboratory for Image and Video Engineering Image Quality Assessment Database and the Tampere Image Database. Performance degradation, as the amount of information is reduced, is also studied.

Objective quality assessment of multiply distorted images

Abstract: Subjective studies have been conducted in the past to obtain human judgments of visual quality on distorted images in order, among other things, to benchmark objective image quality assessment (IQA) algorithms. Existing subjective studies primarily have records of human ratings on images that were corrupted by only one of many possible distortions. However, the majority of images that are available for consumption are corrupted by multiple distortions. Towards broadening the corpora of records of human responses to visual distortions, we recently conducted a study on two types of multiply distorted images to obtain human judgments of the visual quality of such images. Further, we compared the performance of several existing objective image quality measures on the new database and analyze the effects of multiple distortions on commonly used quality-determinant features and on human ratings.

Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

Abstract: We introduce a new video quality database that models video distortions in heavily-trafficked wireless networks and that contains measurements of human subjective impressions of the quality of videos. The new LIVE Mobile Video Quality Assessment (VQA) database consists of 200 distorted videos created from 10 RAW HD reference videos, obtained using a RED ONE digital cinematographic camera. While the LIVE Mobile VQA database includes distortions that have been previously studied such as compression and wireless packet-loss, it also incorporates dynamically varying distortions that change as a function of time, such as frame-freezes and temporally varying compression rates. In this article, we describe the construction of the database and detail the human study that was performed on mobile phones and tablets in order to gauge the human perception of quality on mobile devices. The subjective study portion of the database includes both the differential mean opinion scores (DMOS) computed from the ratings that the subjects provided at the end of each video clip, as well as the continuous temporal scores that the subjects recorded as they viewed the video. The study involved over 50 subjects and resulted in 5,300 summary subjective scores and time-sampled subjective traces of quality. In the behavioral portion of the article we analyze human opinion using statistical techniques, and also study a variety of models of temporal pooling that may reflect strategies that the subjects used to make the final decision on video quality. Further, we compare the quality ratings obtained from the tablet and the mobile phone studies in order to study the impact of these different display modes on quality. We also evaluate several objective image and video quality assessment (IQA/VQA) algorithms with regards to their efficacy in predicting visual quality. A detailed correlation analysis and statistical hypothesis testing is carried out. Our general conclusion is that existing VQA algorithms are not well-equipped to handle distortions that vary over time. The LIVE Mobile VQA database, along with the subject DMOS and the continuous temporal scores is being made available to researchers in the field of VQA at no cost in order to further research in the area of video quality assessment.

Blind Image Quality Assessment Without Human Training Using Latent Quality Factors

Abstract: We propose a highly unsupervised, training free, no reference image quality assessment (IQA) model that is based on the hypothesis that distorted images have certain latent characteristics that differ from those of “natural” or “pristine” images. These latent characteristics are uncovered by applying a “topic model” to visual words extracted from an assortment of pristine and distorted images. For the latent characteristics to be discriminatory between pristine and distorted images, the choice of the visual words is important. We extract quality-aware visual words that are based on natural scene statistic features [1]. We show that the similarity between the probability of occurrence of the different topics in an unseen image and the distribution of latent topics averaged over a large number of pristine natural images yields a quality measure. This measure correlates well with human difference mean opinion scores on the LIVE IQA database [2].

Blind quality assessment of videos using a model of natural scene statistics and motion coherency

Abstract: We propose a no-reference algorithm for video quality evaluation The algorithm relies on a natural scene statistics (NSS) model of video DCT coefficients as well as a temporal model of motion coherency The proposed framework is tested on the LIVE VQA database, and shown to correlate well with human visual judgments of quality

Making image quality assessment robust

Abstract: We develop a robust framework for natural scene statistic (NSS) model based blind image quality assessment (IQA) The robustified IQA model utilizes a robust statistics approach based on L-moments Such robust statistics based approaches are effective when natural or distorted images deviate from assumed statistical models, and achieves better prediction performance on distorted images relative to human subjective judgments We also show how robustifying the model makes IQA approach resilient against deviation in model assumptions, small variations in the distortions and amount of data the model is trained on

A Markov Decision Model for Adaptive Scheduling of Stored Scalable Videos

Abstract: We propose two scheduling algorithms that seek to optimize the quality of scalably coded videos that have been stored at a video server before transmission.} The first scheduling algorithm is derived from a Markov Decision Process (MDP) formulation developed here. We model the dynamics of the channel as a Markov chain and reduce the problem of dynamic video scheduling to a tractable Markov decision problem over a finite state space. Based on the MDP formulation, a near-optimal scheduling policy is computed that minimize the mean square error. Using insights taken from the development of the optimal MDP-based scheduling policy, the second proposed scheduling algorithm is an online scheduling method that only requires easily measurable knowledge of the channel dynamics, and is thus viable in practice. Simulation results show that the performance of both scheduling algorithms is close to a performance upper bound also derived in this paper.

Optimizing Multiscale SSIM for Compression via MLDS

Abstract: A crucial step in the assessment of an image compression method is the evaluation of the perceived quality of the compressed images. Typically, researchers ask observers to rate perceived image quality directly and use these rating measures, averaged across observers and images, to assess how image quality degrades with increasing compression. These ratings in turn are used to calibrate and compare image quality assessment algorithms intended to predict human perception of image degradation. There are several drawbacks to using such omnibus measures. First, the interpretation of the rating scale is subjective and may differ from one observer to the next. Second, it is easy to overlook compression artifacts that are only present in particular kinds of images. In this paper, we use a recently developed method for assessing perceived image quality, maximum likelihood difference scaling (MLDS), and use it to assess the performance of a widely-used image quality assessment algorithm, multiscale structural similarity (MS-SSIM). MLDS allows us to quantify supra-threshold perceptual differences between pairs of images and to examine how perceived image quality, estimated through MLDS, changes as the compression rate is increased. We apply the method to a wide range of images and also analyze results for specific images. This approach circumvents the limitations inherent in the use of rating methods, and allows us also to evaluate MS-SSIM for different classes of visual image. We show how the data collected by MLDS allow us to recalibrate MS-SSIM to improve its performance.

Study of subject agreement on stereoscopic video quality

Abstract: We describe a study that aims towards enhancing our understanding of the perception of H.264/AVC compressed stereoscopic 3D videos, in particular spatial video quality, depth quality, visual comfort and overall 3D video quality. The results of this study indicate that the human subjects have diverse opinions on depth quality scores but a high agreement on spatial video quality. Their agreement on overall 3D video quality is intermediate relative to that on spatial video quality and depth quality. Based on our analysis, we propose to use separate quality assessment models: spatial video quality models and depth quality models.

Automatic parameter prediction for image denoising algorithms using perceptual quality features

Abstract: A natural scene statistics (NSS) based blind image denoising approach is proposed, where denoising is performed without knowledge of the noise variance present in the image. We show how such a parameter estimation can be used to perform blind denoising by combining blind parameter estimation with a state-of-the-art denoising algorithm.1 Our experiments show that for all noise variances simulated on a varied image content, our approach is almost always statistically superior to the reference BM3D implementation in terms of perceived visual quality at the 95% confidence level.

Optimizing 3D image display using the stereoacuity function

Abstract: We develop an algorithm that predicts the best presentation of a stereo 3D image in the sense of viewers' preference. The algorithm operates in three steps. First, the 3D image is classified as either a “foreground dominant” or “background dominant” image. Next, for “foreground dominant” images, a model of the stereoacuity function is used to optimize the perceptual 3D resolution; for “background dominant” images, the nearest surface is placed in the 3D plane of the display screen. A human study was conducted to assess the algorithm and showed that the proposed model produced 3D images which had the best 3D quality scores among several candidate algorithms.

A Flicker Detector Model of the Motion Silencing Illusion

Abstract: Number: 31 Submitted By: Lark Kwon Choi Last Modified: November 22 2011 A Flicker Detector Model of the Motion Silencing Illusion Lark Kwon Choi1,3, Alan Conrad Bovik1,3, Lawrence Kevin Cormack2,3 1Department of Electrical and Computer Engineering, The University of Texas at Austin 2Department of Psychology, The University of Texas at Austin 3Center for Perceptual Systems, The University of Texas at Austin The perception of motion and change are important mechanisms in a visual system. Suchow and Alvarez recently presented a \"motion silencing\" illusion, in which salient flicker (spatially localized repetitive changes in luminance, color, shape, or size) become undetectable in the presence of rapid motion. They also proposed a \"misattribution\" hypothesis, which we interpret to mean that, when there is an actual motion signal, the dynamic signal from the flicker is misattributed to the motion stimulus, and hence no flicker is perceived. In an attempt to understand this phenomenon, we have developed a model incorporating a novel luminance flicker detector. We conducted experiments examining the relationship between rotational velocity (RV) and change rate (CR). We also did a systematic spectral analysis of the stimuli over a wide range of flicker and rotation rates. We then used the distributions of the spectral signatures of the dynamically changing stimuli to develop a computational model of silencing under the assumption that there is a motion energy threshold beyond which all temporal energy is attributed to motion. The model accurately captures the quantitative relationship between RV and CR for silencing, in which linear regression parameters are almost identical between humans and the model. This implies the misattribution hypothesis is likely correct. Specifically, we posit that, given limited resources to detect temporal change, all temporal change is interpreted as motion when a certain amount of actual motion exists. This is understandable in an ecological context because the probable consequences of ignoring true motion (a \"miss\") are likely much greater than misinterpreting flicker as motion (a \"false alarm\") given the relative rarity and importance of stationary flickering stimuli in the natural world. Printable Preview http://www.visionsciences1.org/vss_public/core_routines/print_preview.php?abstractno=31

Full-reference quality assessment of stereoscopic images by modeling binocular rivalry

Abstract: We develop a framework for assessing the quality of stereoscopic images that have been afflicted by possibly asymmetric distortions An intermediate image is generated which when viewed stereoscopically is designed to have a perceived quality close to that of the cyclopean image We hypothesize that performing stereoscopic QA on the intermediate image yields higher correlations with human subjective judgments The experimental results confirm the hypothesis and show that the proposed framework significantly outperforms conventional 2D QA metrics when predicting the quality of stereoscopically viewed images that may have been asymmetrically distorted

Active contours with neighborhood-extending and noise-smoothing gradient vector flow external force

Abstract: We propose a novel external force for active contours, which we call neighborhood-extending and noise-smoothing gradient vector flow (NNGVF). The proposed NNGVF snake expresses the gradient vector flow (GVF) as a convolution with a neighborhood-extending Laplacian operator augmented by a noise-smoothing mask. We find that the NNGVF snake provides better segmentation than the GVF snake in terms of noise resistance, weak edge preservation, and an enlarged capture range. The NNGVF snake accomplishes this with a reduced computational cost while maintaining other desirable properties of the GVF snake, such as initialization insensitivity and good convergences at concavities. We demonstrate the advantages of NNGVF on synthetic and real images.

A nonlinear compound representation of sea clutter

Abstract: We explore the applicability of our recently developed Nonlinear Compound Gaussian (NCG) [1] distribution to modeling the statistics of sea clutter data. We first, for completeness, give a self-contained description of our NCG distribution; both its theoretical properties and the algorithmic details for parameter estimation. We then demonstrate the performance of NCG in modeling the range statistics of sea-clutter data [12]. The results clearly demonstrate the superiority of NCG in modeling sea-clutter phenomena over the compound Gaussian (CG) distribution. We conclude with a brief discussion of a phenomenological interpretation of these results together with directions for future research.

Automatic Feature Extraction and Statistical Shape Model of the AIDS Virus Spike

Abstract: We introduce a method to automatically extract spike features of the AIDS virus imaged through an electron microscope. The AIDS virus spike is the primary target of drug design as it is directly involved in infecting host cells. Our method detects the location of these spikes and extracts a subvolume enclosing the spike. We have achieved a sensitivity of 80% for our best operating range. The extracted spikes are further aligned and combined to build a 4-D statistical shape model, where each voxel in the shape model is assigned a probability density function. Our method is the first fully automated technique that can extract subvolumes of the AIDS virus spike and be used to build a statistical model without the need for any user supervision. We envision that this new tool will significantly enhance the overall process of shape analysis of the AIDS virus spike imaged through the electron microscope. Accurate models of the virus spike will help in the development of better drug design strategies.

A new singularity index

Abstract: 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.

Stereoscopic versus monoscopic detection of masses on breast tomosynthesis projection images

Abstract: The goal of this study was to assess if stereoscopic viewing of breast tomosynthesis projection images impacted mass detection performance when compared to monoscopic viewing. The dataset for this study, provided by Hologic, Inc., contained 47 craniocaudal cases (23 biopsy proven malignant masses and 24 normals). Two projection images that were separated by 8 degrees were chosen to form a stereoscopic pair. The images were preprocessed to enhance their contrast and were presented on a stereoscopic display. Three experienced breast imagers participated in a blinded observer study as readers. Each case was shown twice to each reader - once in the stereoscopic mode, and once in the monoscopic mode in a random order. The readers were asked to make a binary decision on whether they saw a mass for which they would initiate a diagnostic workup or not, and also report the location of the mass and provide a confidence score in the range of 0-100. The binary decisions were analyzed using the sensitivity-specificity measure, while the confidence scores were analyzed using the Receiver Operating Characteristic curve (ROC). We also report a statistical analysis of the difference in partial AUC values greater than 95% sensitivity between the stereoscopic and monoscopic modes.

Statistical model of color and disparity with application to Bayesian stereopsis

Abstract: Extensive research has been conducted relating the natural scene statistics of luminance and depth; however, very little work has been done on analyzing the statistical relationships between depth and chromatic information. In this paper, we examine and derive statistical models between disparity and both luminance and chrominance information by transforming natural images into the more perceptually relevant CIELAB color space. To demonstrate the effectiveness of these models, we further exploit them with application to Bayesian stereo algorithms. The simulation results show that incorporating the derived statistical models augments the performance of Bayesian stereo algorithms. In addition, these results also support psychophysical evidence that chromatic information can improve binocular visual processing.

Computer-Aided Detection and Diagnosis for 3D X-Ray Based Breast Imaging

Abstract: The last 15 years has seen the advent of a variety of powerful 3D x-ray based breast imaging modalities such as digital breast tomosynthesis, digital breast computed tomography, and stereo mammography. These modalities promise to herald a new and exciting future for early detection and diagnosis of breast cancer. In this chapter, the authors review some of the recent developments in 3D x-ray based breast imaging. They also review some of the initial work in the area of computer-aided detection and diagnosis for 3D x-ray based breast imaging. The chapter concludes by discussing future research directions in 3D computer-aided detection. DOI: 10.4018/978-1-4666-0059-1.ch003

The multilinear compound Gaussian distribution

Abstract: We introduce a novel generalization of the compound Gaussian (CG) (or Gaussian Scale Mixture [1]) distribution which extends the Gaussian component of the CG model to a multilinear distribution. The resulting model, which we call the Multilinear Compound Gaussian (MCG) distribution, subsumes both GSM [1] and the previously developed MICA [3–4] distributions as complementary special cases; thereby allowing us to model a richer class of stochastic phenomena. First we derive the structural characterization of the MCG distribution and develop some of its important theoretical properties. Thereafter we describe a parameter estimation algorithm for learning this model from sample data, and then deploy this for modeling textures, including natural (i.e. optical) and SAR images. Our simulation results demonstrate how, for each case, we obtain improved performance over the CG model; thus indicating the versatility of the MCG model in accurately modeling various natural phenomena of interest.

Near-optimal Adaptive Scheduling of Scalable Videos over Wireless Channels

Abstract: We propose two scheduling algorithms that seek to optimize the perceptual quality of scalably coded videos transmitted over slow fading wireless channels. The first scheduling algorithm is derived from a Markov Decision Process (MDP) formulation developed here. We model the dynamics of the channel as a Markov chain and reduce the problem of dynamic video scheduling to a tractable Markov decision problem over a finite state space. Based on the MDP formulation, a near-optimal scheduling policy is computed that maximizes an objective proxy of video quality, the time-average {\em Multi-Scale Structural SIMilarity} (MS-SSIM) index. Using sights token from the development of the optimal MDP-based scheduling policy, the second proposed scheduling algorithm is an online scheduling method that only requires only easily measurable knowledge of the channel dynamics, and is thus viable in practice. Simulation results show that the performance of both scheduling algorithms is close to a performance upper bound also derived in this paper.

Three-dimensional natural scene statistics: dependencies between luminance and range contrasts

Abstract: Computing relative or absolute range (egocentric distance) is difficult because, of course, neither is specified in any direct way by the 2D retinal image. If, however, there was a relationship between range and luminance or color, perhaps it could be exploited to yield fast, initial estimates of range from the retinal image per se. We studied the statistical dependence between range (and disparity) contrast and luminance contrast across random point-pairs in natural scenes, and found that changes in range and luminance are highly dependent. We collected high resolution range maps of natural scenes co-registered with luminance (RGB) images using a Riegl terrestrial scanner, co-mounted camera, and in-house software. Various alternative preprocessing stages were used to simulate the early stages of visual processing (e.g. foveation). Our basic approach was to randomly sample pairs of points in the scenes to determine if the change in range or luminance or both exceeded some criterion. We then 1) compared the conditional density of range edges given luminance edges to the (unconditioned) density of range edges and 2) compared the joint distribution of range and luminance contrast to the product of their marginal distributions. We found a robust statistical dependence between range and luminance. Additionally, we computed difference surface maps (between the joint distributions and product-of-marginals predicted by independence). These difference surfaces reveal which regions of luminance and range change exhibit the strongest statistical dependencies.The statistical dependence between luminance and range allows the construction of models where one can assign a probability of occurrence of a range edge given a luminance edge at a particular point in a scene. In principle, such a mechanism could also be used by biological visual system to serve as priors when reconstructing the 3D environment from 2D image data.

Image Quality Assessment: How Blind is Blind?

Abstract: I will consider some of the factors to be considered when assessing the capability and limitations of a new imaging modality, using holography in the 60's and 70's as an example. Biography (100 word limit): Joseph W. Goodman received an A.B. Degree from Harvard, an M.S degree and a Ph.D. degree, both from Stanford University. He joined the faculty of the Department of Electrical Engineering at Stanford in 1967, chaired the department from 1989 to 1996, and served as Senior Associate Dean of Engineering until 1999. He retired from Stanford in January of 2001. Dr. Goodman is the author of the books Introduction to Fourier Optics (now in its 3 rd edition), Statistical Optics, Speckle Phenomena in Optics. He has received numerous awards from the I.E.E.E., the A.S.E.E., the O.S.A., the S.P.I.E ., including the highest awards given by the latter two societies.Blind Image Quality Assessment (Blind IQA) is usually synonymous with

Unnaturalness Modeling of Image Distortions

Abstract: Natural scene statistic (NSS) models are effective tools for formulating models of early visual processing. One area where NSS models have been successful is predicting human responses to image distortions, or image quality assessment (IQA) by quantifying unnaturalness introduced by distortions. Recent Blind IQA models use NSS features to form predictions of human judgments of distorted image quality without having available corresponding undistorted reference images. Successful learning blind models have previously been developed that learn to accurately predict human opinions of image quality by training them on databases of distorted images and associated human opinion scores. We introduce new NSS feature based blind IQA models that require even less information to attain good results. If human opinion scores of distorted images are not available, but a database of distorted images is, then opinion-less blind IQA models can be created that perform well. We have also found it possible to design blind IQA models without any source of prior information other than a database of distortionless " exemplar " images. An algorithm derived from such a completely blind model has only the distorted image to be quality-assessed available. Our new blind IQA models (Fig. 1) follow four processing steps (Fig. 2). Images are decomposed by an energy compacting filter bank then divisive normalized, yielding responses well-modeled as NSS. Either NSS features alone, or both NSS and distorted image statistic (DSS) features are used to create distributions of visual words. Quality prediction is expressed in terms of the Kullback-Leibler divergence between the distributions of visual words from distorted images and from the space of exemplar images. Both opinion blind and completely blind models compete well with standard non-blind metrics such as mean squared error (MSE) when tested on a large public IQA database (Tables 1 and 2).

Image quality assessment: How Blind is blind?

Abstract: I will consider some of the factors to be considered when assessing the capability and limitations of a new imaging modality, using holography in the 60's and 70's as an example. Biography (100 word limit): Joseph W. Goodman received an A.B. Degree from Harvard, an M.S degree and a Ph.D. degree, both from Stanford University. He joined the faculty of the Department of Electrical Engineering at Stanford in 1967, chaired the department from 1989 to 1996, and served as Senior Associate Dean of Engineering until 1999. He retired from Stanford in January of 2001. Dr. Goodman is the author of the books Introduction to Fourier Optics (now in its 3 rd edition), Statistical Optics, Speckle Phenomena in Optics. He has received numerous awards from the I.E.E.E., the A.S.E.E., the O.S.A., the S.P.I.E ., including the highest awards given by the latter two societies.Blind Image Quality Assessment (Blind IQA) is usually synonymous with

Generating a statistical shape model of the AIDS virus spike

Abstract: We introduce a method to automatically extract the spike features of the AIDS virus imaged through an electron microscope. This method detects the location of the spikes and extracts a sub-volume enclosing the spike with a sensitivity of 80%. The extracted spikes are further aligned and combined to build a 4D statistical shape model, where each voxel in the shape model is assigned a probability density function. This is a fully automated process that can extract sub-volumes of the AIDS virus spike making it possible to build a statistical model without the need for any user supervision. The AIDS virus spike is the primary target of drug design as it is directly involved in infecting host cells. We envision that this new tool will significantly enhance the overall process of shape analysis of the AIDS virus spike imaged through the electron microscope.

Active segmentation of 3D axonal images

Abstract: We present an active contour framework for segmenting neuronal axons on 3D confocal microscopy data. Our work is motivated by the need to conduct high throughput experiments involving microfluidic devices and femtosecond lasers to study the genetic mechanisms behind nerve regeneration and repair. While most of the applications for active contours have focused on segmenting closed regions in 2D medical and natural images, there haven't been many applications that have focused on segmenting open-ended curvilinear structures in 2D or higher dimensions. The active contour framework we present here ties together a well known 2D active contour model [5] along with the physics of projection imaging geometry to yield a segmented axon in 3D. Qualitative results illustrate the promise of our approach for segmenting neruonal axons on 3D confocal microscopy data.