Variations In Image Contrast Research Articles

Adaptive multifeature visual tracking in a probability-hypothesis-density filtering framework

Probability hypothesis density (PHD) based trackers have enjoyed growing popularity in recent years, particularly in the field of nonlinear non-Gaussian visual tracking scenarios. These visual trackers can be degraded by a variety of factors, including changes of illumination, occlusion, poor image contrast, shape and appearance variation, clutter and other unmodeled changes of tracked objects. In this paper, for enhancing the performance of PHD based trackers, both scale invariant feature and color distribution feature are used as descriptors of targets of interest. To adaptively adjust the weights of each feature in the tracking process, a confidence measure, i.e., a quantitative measure for the spatial uncertainty of each feature is incorporated into the multifeature tracking algorithm. Experimental results show that the proposed multifeature tracker can improve the reliability and robustness of state estimation and the number estimation in tracking a variable number of objects of varying scales even when background region was similar to the object's appearance.

Automated chest wall line detection for whole‐breast segmentation in sagittal breast MR images

Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Computerized analysis is increasingly used to quantify breast MRI features in applications such as computer-aided lesion detection and fibroglandular tissue estimation for breast cancer risk assessment. Automated segmentation of the whole-breast as an organ from the other parts imaged is an important step in aiding lesion localization and fibroglandular tissue quantification. For this task, identifying the chest wall line (CWL) is most challenging due to image contrast variations, intensity discontinuity, and bias field. In this work, the authors develop and validate a fully automated image processing algorithm for accurate delineation of the CWL in sagittal breast MRI. The CWL detection is based on an integrated scheme of edge extraction and CWL candidate evaluation. The edge extraction consists of applying edge-enhancing filters and an edge linking algorithm. Increased accuracy is achieved by the synergistic use of multiple image inputs for edge extraction, where multiple CWL candidates are evaluated by the dynamic time warping algorithm coupled with the construction of a CWL reference. Their method is quantitatively validated by a dataset of 60 3D bilateral sagittal breast MRI scans (in total 3360 2D MR slices) that span the full American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density range. Agreement with manual segmentation obtained by an experienced breast imaging radiologist is assessed by both volumetric and boundary-based metrics, including four quantitative measures. In terms of breast volume agreement with manual segmentation, the overlay percentage expressed by the Dice's similarity coefficient is 95.0% and the difference percentage is 10.1%. More specifically, for the segmentation accuracy of the CWL boundary, the CWL overlay percentage is 92.7% and averaged deviation distance is 2.3 mm. Their method requires ≈ 4.5 min for segmenting each 3D breast MRI scan (56 slices) in comparison to ≈ 35 min required for manual segmentation. Further analysis indicates that the segmentation performance of their method is relatively stable across the different BI-RADS density categories and breast volume, and also robust with respect to a varying range of the major parameters of the algorithm. Their fully automated method achieves high segmentation accuracy in a time-efficient manner. It could support large scale quantitative breast MRI analysis and holds the potential to become integrated into the clinical workflow for breast cancer clinical applications in the future.

Performance Evaluation in Iris Recognition and CBIR System based on Phase Congruency

based image retrieval is an important research area in image processing, with a vast domain of applications like recognition systems i.e. face, finger, iris biometric etc. It retrieves the similar type of images from repository of images based on users query. To retrieve similar images, color, and texture or shape features need to be extracted from the images and stored in the feature database. The color, texture or shape features of query image are compared with the features of images in the database. This comparison is performed using color, texture or shape distance metrics. Phase Congruency is applied to the input image for feature extraction. Advantage of the Phase Congruency method is its insensitivity to variations in image illumination and contrast. Like CBIR, the methods for iris recognition mainly focus on feature representation and matching. We use phase congruency to generate iris feature vector. Phase Congruency can be calculated using Log Gabor wavelets. The goal of this paper is to evaluate the performance of CBIR system and iris recognition that use Phase Congruency. To evaluate performance in CBIR System, precision and recall measures are used. In order to evaluate iris recognition system, recognition rate measure is used and compared with existing methods. Results of performance evaluation are discussed in paper.

Optimal Setting of Automatic Exposure Control Based on Image Noise and Contrast on Iodine-enhanced CT

The aim of this study was to investigate variations in image noise and contrast using automatic exposure control (AEC) and different tube voltages on nonenhanced and iodine-enhanced hepatic computed tomography. Nonenhanced and iodine-enhanced simulated liver phantoms and AEC were used. Tube current was automatically adjusted with the noise index. Two types of assessments were performed: at a fixed noise index of 10 Hounsfield units and at different noise indexes, keeping the same contrast-to-noise ratio at different tube voltages (100, 120, and 130 kV). Image noise was measured, and contrast between the computed tomographic number of the simulated liver and nodule was computed. At a fixed noise index, image noise on iodine-enhanced images was 10% to 13% higher than on nonenhanced images at the same tube voltage. At 130 and 100 kV, contrast was 33.86 and 46.90 Hounsfield units, respectively, and image noise was almost the same. Contrast-to-noise ratios at 100, 120, and 130 kV were 3.31, 3.22, and 3.37, respectively, and volume computed tomographic dose index fell from 22.94 to 12.49 mGy with decreasing tube voltage. With AEC, image noise on iodine-enhanced images was higher than on nonenhanced images despite identical noise index settings. As tube voltage decreased, contrast on iodine-enhanced images increased. Considering noise index and contrast variations at different tube voltages, the optimal use of AEC on iodine-enhanced computed tomography facilitates a reduction in x-ray tube output while maintaining contrast-to-noise ratio.

Prostate segmentation by sparse representation based classification.

Accurate segmentation of prostate in CT images is important in image-guided radiotherapy. However, it is difficult to localize the prostate in CT images due to low image contrast, unpredicted motion and large appearance variations across different treatment days. To address these issues, we propose a sparse representation based classification method to accurately segment the prostate. The main contributions of this paper are: (1) A discriminant dictionary learning technique is proposed to overcome the limitation of the traditional Sparse Representation based classifier (SRC). (2) Context features are incorporated into SRC to refine the prostate boundary in an iterative scheme. (3) A residue-based linear regression model is trained to increase the classification performance of SRC and extend it from hard classification to soft classification. To segment the prostate, the new treatment image is first rigidly aligned to the planning image space based on the pelvic bones. Then two sets of location-adaptive SRCs along two coordinate directions are applied on the aligned treatment image to produce a probability map, based on which all previously segmented images of the same patient are rigidly aligned onto the new treatment image and majority voting strategy is further adopted to finally segment the prostate in the new treatment image. The proposed method has been evaluated on a CT dataset consisting of 15 patients and 230 CT images. Promising results have been achieved.

Effect of Image Quality, Color, and Format on the Measurement of Retinal Vascular Fractal Dimension

Fractal dimension of retinal vasculature is a global summary measure of retinal vascular network pattern and geometry. This study was conducted to examine the effect of variations in image color, brightness, focus, contrast, and format on the measurement of retinal vascular fractal dimension. A set of 30 retinal images from the Blue Mountains Eye Study was used for a series of experiments by varying brightness, focus (blur), contrast, and color (color versus monochrome). The original and the modified images were graded for fractal dimension (D(f)) using dedicated retinal imaging software (IRIS-Fractal). A further set of 20 grayscale images was used to compare image format (.jpg versus .tif) with regard to the resultant D(f) and processing time. The mean D(f) of original images in this sample was 1.454. Compared with the original set of images, variations in brightness, focus, contrast, and color affected the measurements to a small to moderate degree (Pearson correlation coefficient, r, ranged from 0.47 to 0.97). Very dark or blurry images resulted in a substantially lower estimate of D(f). Monochrome images were also consistently associated with lower D(f) compared with that obtained from color images. Using .jpg or .tif image formats did not affect the measurement or the time needed to process and measure D(f). Variations in image brightness, focus, and contrast can significantly affect the measurement of retinal vascular fractals. Standardization of image parameters and consistent use of either monochrome or color images would reduce measurement noise and enhance the comparability of the results.

TU‐A‐204B‐03: 3‐D Fiducial Localization with MV EPID Imaging during a RapidArc Therapy

Purpose: Fiducials can be used to aid organ localization during RapidArc delivery. Specifically, the 3‐D tumor position may be found through the tracking of fiducials during beam delivery. A promising technique is the detection of fiducials in 2‐D EPID images, and then reconstruction of the 3‐D position using limited views. Methods and Materials: The CIRS dynamic thorax phantom with a custom tumor model is used in the study. Two fiducials are placed on the surface of the tumor at clinically relevant distances apart from each other. Clinical RapidArc plans are delivered to the phantom based on a single 360° arc. The fiducial detection algorithms are developed based on an image block matching filter and anisotropic diffusion techniques. A wavelet‐based block matching filter is developed to suppress the noise and texture of images while preserving the edges of fiducials. Furthermore, the anisotropic diffusion technique removes redundant edges and trivial structures of the results of the previous filtering. Experiments show that the algorithms are robust to image contrast and aperture variations during the RapidArc delivery. With detected 2‐D locations, 3‐D spatial locations of fiducials are reconstructed using the inverse 3‐D rotation matrix. Results: The position accuracy in 2D EPID is 0.77+0.58 mm by comparing with the manual outlines. The 3D fiducial positions are reconstructed using the 2D EPID images. The displacement from the references in the 3D CBCT image is within one millimeter. Conclusion: Novel filtering and detection algorithms are developed to identify the fiducials in 2D EPID images. The results show that limited angles of full rotation can rigorously reconstruct the locations of each of fiducials in 3D to within one millimeter. Conflict of Interest: Varian Medical Systems, Inc.

Cancer Cases from ACRIN Digital Mammographic Imaging Screening Trial: Radiologist Analysis with Use of a Logistic Regression Model

To determine which factors contributed to the Digital Mammographic Imaging Screening Trial (DMIST) cancer detection results. This project was HIPAA compliant and institutional review board approved. Seven radiologist readers reviewed the film hard-copy (screen-film) and digital mammograms in DMIST cancer cases and assessed the factors that contributed to lesion visibility on both types of images. Two multinomial logistic regression models were used to analyze the combined and condensed visibility ratings assigned by the readers to the paired digital and screen-film images. Readers most frequently attributed differences in DMIST cancer visibility to variations in image contrast--not differences in positioning or compression--between digital and screen-film mammography. The odds of a cancer being more visible on a digital mammogram--rather than being equally visible on digital and screen-film mammograms--were significantly greater for women with dense breasts than for women with nondense breasts, even with the data adjusted for patient age, lesion type, and mammography system (odds ratio, 2.28; P < .0001). The odds of a cancer being more visible at digital mammography--rather than being equally visible at digital and screen-film mammography--were significantly greater for lesions imaged with the General Electric digital mammography system than for lesions imaged with the Fischer (P = .0070) and Fuji (P = .0070) devices. The significantly better diagnostic accuracy of digital mammography, as compared with screen-film mammography, in women with dense breasts demonstrated in the DMIST was most likely attributable to differences in image contrast, which were most likely due to the inherent system performance improvements that are available with digital mammography. The authors conclude that the DMIST results were attributable primarily to differences in the display and acquisition characteristics of the mammography devices rather than to reader variability.

Transmission electron microscopy study of one-dimensionalincommensurate structural modulation in superconducting oxidesBi2+xSr2−xCuO6+δ (0.10≤x≤0.40)

The one-dimensional incommensurate structural modulation inBi2+xSr2−xCuO6+δ (0.10≤x≤0.40) has been studied by electron diffraction and high-resolution electron microscopy. Themodulation wave vectors and four-dimensional symmetry were determined from electrondiffraction patterns. The quality of high-resolution electron microscope images was improvedby noise filtering and symmetry averaging so that the displacive and compositionalmodulation could be seen in images much more clearly than usual. The atomic substitutionin Bi and Sr sites was analyzed based on the image contrast variation and the arrangementof Bi-concentrated regions was studied. The dependence of modulation wave vectors,twinning modulation and Bi-concentrated regions on the amount of Bi doping,x, is demonstrated. By considering the relationship betweenTc andx reported by Luo et al (2007 J. Cryst. Growth 305 222) the relationship between thetransition temperature and incommensurate modulated structure is discussed.

Quantitative Image Contrast Variations in STEM

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

Automatic image‐driven segmentation of the ventricles in cardiac cine MRI

To propose and to evaluate a novel method for the automatic segmentation of the heart's two ventricles from dynamic ("cine") short-axis "steady state free precession" (SSFP) MR images. This segmentation task is of significant clinical importance. Previously published automated methods have various disadvantages for routine clinical use. The proposed method is primarily image-driven: it exploits the spatiotemporal information provided by modern 3D+time SSFP cardiac MRI, and makes only few and plausible assumptions about the image acquisition and about the imaged heart. Specifically, the method does not require previously trained statistical shape models or gray-level appearance models, as often used by other methods. The performance of the segmentation method was demonstrated through a qualitative visual validation on 32 clinical exams: no gross failures for the left-ventricle (right-ventricle) on 31 (29) of the exams were found. A validation of resulting quantitative cardiac functional parameters showed good agreement with a manual quantification of 19 clinical exams. The proposed method is feasible, fast, and robust against anatomical variability and image contrast variations.

Assessment of change of optic nerve head cupping in pediatric glaucoma using the RetCam 120

To ascertain the interobserver agreement among various specialists when analyzing images of the optic nerve head taken with the RetCam 120 (Clarity Medical Systems, Inc., Pleasanton, CA) for assessing the progression of optic disk cupping in pediatric glaucoma. Five observers compared pairs of RetCam 120 two-dimensional photographs from 64 eyes to identify whether or not changes in the optic disk had occurred. Observers included a pediatric glaucoma specialist, pediatric neurophthalmologist, strabismologist, pediatric ophthalmic imaging specialist, and pediatric ophthalmology fellow. Each was masked to the patient identity and clinical course. For each patient, the observers reviewed the photographs taken prior to therapy and at a minimum of 6 months following initiation of therapy. Interobserver variability was calculated and analysis conducted to identify influential variables. Poor agreement was noted between the observers. No significant association was obtained between agreement level and variables affecting photographic quality: variability of contrast and illumination, exposure and magnification, image clarity, presence of fluorescein on the cornea, and pixelation of the image with poor resolution. Raters noted problems with these image variables in 75.6% of the observations. The Kappa statistic obtained was kappa = 0.29 with overall proportion of agreement of p = 0.69, suggesting a fair agreement but not a moderate agreement. Comparison of RetCam 120 pediatric optic nerve head photographs, when performed subjectively by observers with different perspectives, and in isolation from clinical information, may not be a reliable indicator of cupping change. In addition, variations in image contrast, luminance, color, and pixelation pose significant challenges to agreement between observers. Comparison of RetCam 120 images should not be considered the sole criterion for monitoring pediatric glaucoma.

Crystal size and shape analysis of Pt nanoparticles in two and three dimensions

The majority of industrial catalysts are high-surface-area solids, onto which an active component is dispersed in the form of nanoparticles that have sizes of between 1 and 20 nm. In an industrial environment, the crystal size distributions of such particles are conventionally measured by using either bright-field transmission electron microscope (TEM) images or X-ray diffraction. However, the analysis of particle sizes and shapes from two-dimensional bright-field TEM images is affected by variations in image contrast between adjacent particles, by the difficulty of distinguishing the particles from their matrix, and by overlap between particles when they are imaged in projection. High-angle annular dark-field (HAADF) electron tomography provides a convenient technique for overcoming many of these problems, by allowing the three-dimensional shapes and sizes of high atomic number nanoparticles that are supported on a low atomic number support to be recorded. Here, we discuss the three-dimensional analysis of particle sizes and shapes from such tomographic data, and we assess whether such measurements provide different information from that obtained using two-dimensional TEM images and X-ray diffraction measurements.

Are we in the dark about reading medical images?

Medical imaging specialists such as radiologists spend much of their time viewing x-ray and other images, traditionally on film but now often on computer monitors. They need to be able to spot abnormalities that show up only as subtle variations in image contrast. To make the most of the available image contrast, viewing is carried out under controlled conditions in specialist ‘reading rooms’, generally with low levels of ambient light. Low lighting levels, however, may cause eye-strain that reduces diagnostic performance and efficiency. 2 This eyestrain may be because the eyes have to swiftly adapt to the bright monitor screen and then the much darker surrounding surfaces when interpreting images. Preliminary studies have concluded that consistent luminance levels between medical monitors and background surfaces can eliminate eye-strain in radiologists who have previously complained of it. 4 To date there has been no serious attempt to increase ambient light levels in reading rooms because of concerns that doing so will reduce the ability to spot subtle variations in image contrast, especially in darker areas of the image. Current standards for the design of reading rooms and the calibration of medical monitors therefore take little account of the problem of visual fatigue. But with the advent of liquid crystal displays (LCDs) as the display of choice for many radiology reading rooms, a way to safely increase ambient lighting may finally be in sight. Currently available LCDs offer display properties that enable flexibility in setting ambient light levels while keeping contrast degradation under control. The methodology presented here exploits the properties of LCDs to allow reading rooms to be illuminated in a way that minimizes the luminance adaptation required of the eye—potentially reducing visual fatigue—while maximizing visible image contrast. The approach is based on the adaptation characteristics of the human visual system and on advantageous properties of LCDs, Figure 1. Variation of f with ambient illuminance for surfaces with different reflection coefficients, Rs, in a typical radiology reading room. Here, f = |Ls − Ladp| is the difference between the adaptation luminance level of the eye when looking at a medical image on screen, and the luminance from surfaces in the reading room. The monitor is an LCD calibrated with a maximum luminance of 600 lux and a luminance ratio of 600. f decreases with increased illumination of the room. Also shown is the resultant change in the effective luminance ratio on the display (solid line), and the two conditions imposed on the effective luminance ratio by the American Association of Physicists in Medicine (horizontal lines).

Measuring line roughness through aerial image contrast variation using coherent extreme ultraviolet spatial filtering techniques

Line edge roughness properties for an extreme ultraviolet photoresist (Rohm and Haas/Shipley 1K) were investigated by varying the aerial image contrast of dense line and space patterns. Aerial image contrast variation was performed in single exposures by programming the modulation information on the mask. No background flood exposures were needed to reduce the contrast. The Micro Exposure Tool at Lawrence Berkeley National Laboratory was used for these experiments. Dense 50nm lines and spaces were printed with contrast levels ranging from 86.4% to 46.8%. Coherence was programmed to be 0.1 for these experiments. Results show that an increase in the aerial image contrast causes a subsequent decrease in the line edge roughness (LER). Similar effects are seen for linewidth roughness (LWR). The LER varied from 3.3nm (at 86.4% contrast) to 8.0nm (at 46.8% contrast). LWR varied from 5.3nm (at 86.4% contrast) to 12.8nm (at 46.8% contrast). All values are three sigma root-mean-square. Only a couple of dense 30nm features would print in this configuration. For these 30nm lines and spaces, the best LER was 5.6nm and LWR was 11.3nm.

Contrast Adaptation and Representation in Human Early Visual Cortex

The human visual system can distinguish variations in image contrast over a much larger range than measurements of the static relationship between contrast and response in visual cortex would suggest. This discrepancy may be explained if adaptation serves to re-center contrast response functions around the ambient contrast, yet experiments on humans have yet to report such an effect. By using event-related fMRI and a data-driven analysis approach, we found that contrast response functions in V1, V2, and V3 shift to approximately center on the adapting contrast. Furthermore, we discovered that, unlike earlier areas, human V4 (hV4) responds positively to contrast changes, whether increments or decrements, suggesting that hV4 does not faithfully represent contrast, but instead responds to salient changes. These findings suggest that the visual system discounts slow uninformative changes in contrast with adaptation, yet remains exquisitely sensitive to changes that may signal important events in the environment.

Time-scale change detection applied to real-time abnormal stationarity monitoring

This paper presents two robust algorithms with respect to global contrast changes: one detects changes; and the other detects stationary people or objects in image sequences obtained via a fixed camera. The first one is based on a level set representation of images and exploits their suitable properties under image contrast variation. The second makes use of the first, at different time scales, to allow discriminating between the scene background, the moving parts and stationarities. This latter algorithm is justified by and tested in real-life situations; the detection of abnormal stationarities in public transit settings, e.g. subway corridors, will be presented herein with assessments carried out on a large number of real-life situations.

Scanning tunneling microscopy image contrast variation on a copper (II) complex adlayer with tip–sample separation

Contrast variation of scanning tunneling microscopy (STM) image has been investigated as a function of tip–sample separation on a bis[1,3-di( p- n-tetradecyloxyphenyl)propane-1,3-dionato]copper(II) (abbreviated as C 14O–Cu) adlayer on highly oriented pyrolytic graphite with STM. Submolecular features of the molecular core and interdigitated alkyl chain are clearly revealed. A preliminary theoretical calculation is carried out to simulate the image variation. The simulated results are in agreement with STM observation.

Deep-ultraviolet interferometric lithography as a tool for assessment of chemically amplified photoresist performance

The precise control of the exposure step provided by interferometric photolithography facilitates studies of chemically amplified resist physics, chemistry, and functional properties that are difficult using more conventional exposure techniques. We describe here the design and operating characteristics of a deep-ultraviolet interferometric lithography tool designed specifically for the study of high resolution chemically amplified resists. We provide an example of its use to evaluate resist response to controlled variations in aerial image contrast.

Atomic arrangement at the AlN/SiC interface.

The lattice structure of the AlN/SiC interface has been studied in cross section by high-resolution transmission-electron microscopy. Lattice images show planar and crystallographically abrupt interfaces. The atomic arrangement at the plane of the interface is analyzed based on the image characteristics. Possible bonding configurations are discussed. Variations in local image contrast and interplanar separations are used to identify atomic bonding configurations consistent with the lattice images. \textcopyright{} 1996 The American Physical Society.