Biomedical subjects

Alan C Bovik

Publications and source records attributed to Alan C Bovik.

8 recordsLinked to original sources

PubMed2006

Visual search in noise: revealing the influence of structural cues by gaze-contingent classification image analysis.

Visual search experiments have usually involved the detection of a salient target in the presence of distracters against a blank background. In such high signal-to-noise scenarios, observers have been shown to use visual cues such as color, size, and shape of the target to program their saccades during visual search. The degree to which these features affect search performance is usually measured using reaction times and detection accuracy. We asked whether human observers are able to use target features to succeed in visual search tasks in stimuli with very low signal-to-noise ratios. Using the classification image analysis technique, we investigated whether observers used structural cues to direct their fixations as they searched for simple geometric targets embedded at very low signal-to-noise ratios in noise stimuli that had the spectral characteristics of natural images. By analyzing properties of the noise stimulus at observers' fixations, we were able to reveal idiosyncratic, target-dependent features used by observers in our visual search task. We demonstrate that even in very noisy displays, observers do not search randomly, but in many cases they deploy their fixations to regions in the stimulus that resemble some aspect of the target in their local image features.

Artifacts↗

PubMed2006

Image information and visual quality.

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or similarity with a "reference" or "perfect" image in some perceptual space. Such "full-reference" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by signal fidelity measures. In this paper, we approach the image QA problem as an information fidelity problem. Specifically, we propose to quantify the loss of image information to the distortion process and explore the relationship between image information and visual quality. QA systems are invariably involved with judging the visual quality of "natural" images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of such natural signals. Using these models, we previously presented an information fidelity criterion for image QA that related image quality with the amount of information shared between a reference and a distorted image. In this paper, we propose an image information measure that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image. Combining these two quantities, we propose a visual information fidelity measure for image QA. We validate the performance of our algorithm with an extensive subjective study involving 779 images and show that our method outperforms recent state-of-the-art image QA algorithms by a sizeable margin in our simulations. The code and the data from the subjective study are available at the LIVE website.

Algorithms↗

PubMed2006

A joint source-channel distortion model for JPEG compressed images.

The need for efficient joint source-channel coding (JSCC) is growing as new multimedia services are introduced in commercial wireless communication systems. An important component of practical JSCC schemes is a distortion model that can predict the quality of compressed digital multimedia such as images and videos. The usual approach in the JSCC literature for quantifying the distortion due to quantization and channel errors is to estimate it for each image using the statistics of the image for a given signal-to-noise ratio (SNR). This is not an efficient approach in the design of real-time systems because of the computational complexity. A more useful and practical approach would be to design JSCC techniques that minimize average distortion for a large set of images based on some distortion model rather than carrying out per-image optimizations. However, models for estimating average distortion due to quantization and channel bit errors in a combined fashion for a large set of images are not available for practical image or video coding standards employing entropy coding and differential coding. This paper presents a statistical model for estimating the distortion introduced in progressive JPEG compressed images due to quantization and channel bit errors in a joint manner. Statistical modeling of important compression techniques such as Huffman coding, differential pulse-coding modulation, and run-length coding are included in the model. Examples show that the distortion in terms of peak signal-to-noise ratio (PSNR) can be predicted within a 2-dB maximum error over a variety of compression ratios and bit-error rates. To illustrate the utility of the proposed model, we present an unequal power allocation scheme as a simple application of our model. Results show that it gives a PSNR gain of around 6.5 dB at low SNRs, as compared to equal power allocation.

Algorithms↗

PubMed2005

A new technique for digital fluoroscopic video assessment of sagittal plane lumbar spine motion.

STUDY DESIGN: Methodological reliability. OBJECTIVE: Develop a measurement technique to assess dynamic motion of the lumbar spine using enhanced digital fluoroscopic video (DFV) and a distortion compensated roentgen analysis (DCRA). SUMMARY OF BACKGROUND DATA: Controversy over both the definition and consequences of lumbar segmental instability persists. Information from static imaging has had limited success in providing an understanding of this disorder. DFV has the potential to provide further information about lumbar segmental instability; however, the image quality is poor and clinical application is limited. METHODS: DFV from 20 male subjects (11 with and nine without low back pain) were obtained during eccentric lumbar flexion (30 Hz). Each DFVs was enhanced with a series of filters to accentuate the vertebral edges. An adapted DCRA algorithm was applied to determine segmental angular and linear displacement. Both intraimage and interimage reliability were assessed using intraclass correlation coefficients (ICC) and standard error of the measurement (SEM). RESULTS.: Intraimage reliability yielded an average ICC of 0.986, and the SEM ranged from 0.4-0.7 degrees and 0.2-0.3 mm. Interimage reliability yielded an average ICC of 0.878, and the SEM ranged from 0.7-1.4 degrees and 0.4-0.7 mm. CONCLUSIONS: Enhanced DFV combined with a DCRA resulted in reliable assessment of lumbar spine kinematics. The error values associated with this technique were low and were comparable to published error measurements obtained when using a similar algorithm on hand-drawn outlines from static radiographs.

Adult↗

PubMed2005

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images.

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We (1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.

Algorithms↗

PubMed2005

Contrast statistics for foveated visual systems: fixation selection by minimizing contrast entropy.

The human visual system combines a wide field of view with a high-resolution fovea and uses eye, head, and body movements to direct the fovea to potentially relevant locations in the visual scene. This strategy is sensible for a visual system with limited neural resources. However, for this strategy to be effective, the visual system needs sophisticated central mechanisms that efficiently exploit the varying spatial resolution of the retina. To gain insight into some of the design requirements of these central mechanisms, we have analyzed the effects of variable spatial resolution on local contrast in 300 calibrated natural images. Specifically, for each retinal eccentricity (which produces a certain effective level of blur), and for each value of local contrast observed at that eccentricity, we measured the probability distribution of the local contrast in the unblurred image. These conditional probability distributions can be regarded as posterior probability distributions for the "true" unblurred contrast, given an observed contrast at a given eccentricity. We find that these conditional probability distributions are adequately described by a few simple formulas. To explore how these statistics might be exploited by central perceptual mechanisms, we consider the task of selecting successive fixation points, where the goal on each fixation is to maximize total contrast information gained about the image (i.e., minimize total contrast uncertainty). We derive an entropy minimization algorithm and find that it performs optimally at reducing total contrast uncertainty and that it also works well at reducing the mean squared error between the original image and the image reconstructed from the multiple fixations. Our results show that measurements of local contrast alone could efficiently drive the scan paths of the eye when the goal is to gain as much information about the spatial structure of a scene as possible.

Algorithms↗

PubMed2002

Direct Raman imaging techniques for study of the subcellular distribution of a drug.

Direct Raman imaging techniques are demonstrated to study the drug distribution in living cells. The advantage of Raman imaging is that no external markers are required, which simplifies the sample preparation and minimally disturbs the drug mechanism during imaging. The major challenge in Raman imaging is the weak Raman signal. In this study, we present a Raman image model to describe the degradation of Raman signals by imaging processes. Using this model, we demonstrate special-purpose image-processing algorithms to restore the Raman images. The processing techniques are then applied to visualize the anticancer agent paclitaxel in living MDA-435 breast cancer cells. Raman images were obtained from a cancer cell before, during, and after drug treatment. The paclitaxel distribution illustrated in these images is explained by means of the binding characteristics of the paclitaxel and its molecular target-the microtubules. This result demonstrates that direct Raman imaging is a promising tool to study the distribution of a drug in living cells.

Antineoplastic Agents, Phytogenic↗

PubMed2002

Smoothing low-SNR molecular images via anisotropic median-diffusion.

We propose a new anisotropic diffusion filter for denoising low-signal-to-noise molecular images. This filter, which incorporates a median filter into the diffusion steps, is called an anisotropic median-diffusion filter. This hybrid filter achieved much better noise suppression with minimum edge blurring compared with the original anisotropic diffusion filter when it was tested on an image created based on a molecular image model. The universal quality index, proposed in this paper to measure the effectiveness of denoising, suggests that the anisotropic median-diffusion filter can retain adherence to the original image intensities and contrasts better than other filters. In addition, the performance of the filter is less sensitive to the selection of the image gradient threshold during diffusion, thus making automatic image denoising easier than with the original anisotropic diffusion filter. The anisotropic median-diffusion filter also achieved good denoising results on a piecewise-smooth natural image and real Raman molecular images.

Anisotropy↗