Variations In Image Brightness Research Articles

Sequence Image Registration for Large Depth of Microscopic Focus Stacking

The large depth of images for microscopic measurement can be achieved by using focus stacking techniques with a small depth of field of objective lens. It is implemented by fusing the image sequences of short depth images. However, the non-linear movement of the objective imaging system or the measured object caused by the moving stage straightness error brings the misalignment of the image sequences, such as transversal translation, rotation, and tilting. All of these interferences, as well as the image brightness variation must be corrected by image registration before fusing the image sequences. In this paper, a fast-automatic registration method based on the scale invariant feature transform (SIFT) is proposed. It is achieved by firstly segmenting the focal regions of the image sequences through fast edge detection. Then the image features are extracted within the small segmented focal areas. It greatly reduces the computational cost of feature extraction and the following steps of image correction, and alignment. In the process, the random sampling consistency (RANSAC) algorithm is also used to remove the mistake features. The Laplacian pyramid method is adopted for the large depth of image fusion. The experimental results show that the proposed method is more efficient than the traditional SIFT algorithm. Its registration efficiency is improved by about 60%. This method facilitates the high-precision and real-time imaging of a monocular three-dimensional focus stacking.

Commissioning of a fluoroscopic-based real-time markerless tumor tracking system in a superconducting rotating gantry for carbon-ion pencil beam scanning treatment.

To perform the final quality assurance of our fluoroscopic-based markerless tumor tracking for gated carbon-ion pencil beam scanning (C-PBS) radiotherapy using a rotating gantry system, we evaluated the geometrical accuracy and tumor tracking accuracy using a moving chest phantom with simulated respiration. The positions of the dynamic flat panel detector (DFPD) and x-ray tube are subject to changes due to gantry sag. To compensate for this, we generated a geometrical calibration table (gantry flex map) in 15° gantry angle steps by the bundle adjustment method. We evaluated five metrics: (a) Geometrical calibration was evaluated by calculating chest phantom positional error using 2D/3D registration software for each 5° step of the gantry angle. (b) Moving phantom displacement accuracy was measured (±10mm in 1-mm steps) with a laser sensor. (c) Tracking accuracy was evaluated with machine learning (ML) and multi-template matching (MTM) algorithms, which used fluoroscopic images and digitally reconstructed radiographic (DRR) images as training data. The chest phantom was continuously moved ±10mm in a sinusoidal path with a moving cycle of 4s and respiration was simulated with ±5mm expansion/contraction with a cycle of 2s. This was performed with the gantry angle set at 0°, 45°, 120°, and 240°. (d) Four types of interlock function were evaluated: tumor velocity, DFPD image brightness variation, tracking anomaly detection, and tracking positional inconsistency in between the two corresponding rays. (e) Gate on/off latency, gating control system latency, and beam irradiation latency were measured using a laser sensor and an oscilloscope. By applying the gantry flex map, phantom positional accuracy was improved from 1.03mm/0.33° to <0.45mm/0.27° for all gantry angles. The moving phantom displacement error was 0.1mm. Due to long computation time, the tracking accuracy achieved with ML was <0.49mm (=95% confidence interval [CI]) for imaging rates of 15 and 7.5fps; those at 30fps were decreased to 1.84mm (95% CI: 1.79mm-1.92mm). The tracking positional accuracy with MTM was <0.52mm (=95% CI) for all gantry angles and imaging frame rates. The tumor velocity interlock signal delay time was 44.7ms (=1.3 frame). DFPD image brightness interlock latency was 34ms (=1.0 frame). The tracking positional error was improved from 2.27±2.67mm to 0.25±0.24mm by the tracking anomaly detection interlock function. Tracking positional inconsistency interlock signal was output within 5.0ms. The gate on/off latency was <82.7±7.6ms. The gating control system latency was <3.1±1.0ms. The beam irradiation latency was <8.7±1.2ms. Our markerless tracking system is now ready for clinical use. We hope to shorten the computation time needed by the ML algorithm at 30fps in the future.

Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information

Although river discharge is a fundamental hydrologic quantity, conventional methods of streamgaging are impractical, expensive, and potentially dangerous in remote locations. This study evaluated the potential for measuring discharge via various forms of remote sensing, primarily thermal imaging of flow velocities but also spectrally-based depth retrieval from passive optical image data. We acquired thermal image time series from bridges spanning five streams in Alaska and observed strong agreement between velocities measured in situ and those inferred by Particle Image Velocimetry (PIV), which quantified advection of thermal features by the flow. The resulting surface velocities were converted to depth-averaged velocities by applying site-specific, calibrated velocity indices. Field spectra from three clear-flowing streams provided strong relationships between depth and reflectance, suggesting that, under favorable conditions, spectrally-based bathymetric mapping could complement thermal PIV in a hybrid approach to remote sensing of river discharge; this strategy would not be applicable to larger, more turbid rivers, however. A more flexible and efficient alternative might involve inferring depth from thermal data based on relationships between depth and integral length scales of turbulent fluctuations in temperature, captured as variations in image brightness. We observed moderately strong correlations for a site-aggregated data set that reduced station-to-station variability but encompassed a broad range of depths. Discharges calculated using thermal PIV-derived velocities were within 15% of in situ measurements when combined with depths measured directly in the field or estimated from field spectra and within 40% when the depth information also was derived from thermal images. The results of this initial, proof-of-concept investigation suggest that remote sensing techniques could facilitate measurement of river discharge.

Impact of Altering Various Image Parameters on Human Epidermal Growth Factor Receptor 2 Image Analysis Data Quality

Introduction: The quality of data obtained from image analysis can be directly affected by several preanalytical (e.g., staining, image acquisition), analytical (e.g., algorithm, region of interest [ROI]), and postanalytical (e.g., computer processing) variables. Whole-slide scanners generate digital images that may vary depending on the type of scanner and device settings. Our goal was to evaluate the impact of altering brightness, contrast, compression, and blurring on image analysis data quality. Methods: Slides from 55 patients with invasive breast carcinoma were digitized to include a spectrum of human epidermal growth factor receptor 2 (HER2) scores analyzed with Visiopharm (30 cases with score 0, 10 with 1+, 5 with 2+, and 10 with 3+). For all images, an ROI was selected and four parameters (brightness, contrast, JPEG2000 compression, out-of-focus blurring) then serially adjusted. HER2 scores were obtained for each altered image. Results: HER2 scores decreased with increased illumination, higher compression ratios, and increased blurring. HER2 scores increased with greater contrast. Cases with HER2 score 0 were least affected by image adjustments. Conclusion: This experiment shows that variations in image brightness, contrast, compression, and blurring can have major influences on image analysis results. Such changes can result in under- or over-scoring with image algorithms. Standardization of image analysis is recommended to minimize the undesirable impact such variations may have on data output.

A Robust Vision-Based Skyline Detection Algorithm Under Different Weather Conditions

The skyline is a piece of important reference information in the automatic flight control system of an unmanned vehicle. Currently, most skyline detection algorithms assume that the skyline has distinct edge features that can be located in the image by edge detection. This method of detection, however, is only applicable when the skyline is obvious. Its detection result in images with an indistinct skyline is affected by the threshold value, such as on cloudy or heavily foggy days, when clear skyline features cannot be found. To find both distinct and indistinct skylines in the images, this paper proposes a vision-based skyline detection algorithm, in which the skyline is located by analyzing image brightness variations. This method is able to identify nonlinear skyline profiles in scenes with a clear skyline, and find the interface region between the sky and earth in scenes with an indistinct skyline, to estimate its location. According to the experimental results, the algorithm correctly finds the skyline profile in 97% of the test images, making it ideal to provide skyline reference information for the automatic flight control systems of unmanned vehicles.

DEVELOPMENT OF 2D ADAPTIVE METHOD OF ESTIMATION REPRODUCTION FOR IMAGE RECONSTRUCTION IN INCOMPLETE A PRIORI INFORMATION

A method for two-dimensional signal reconstruction in white-and-black and color image processing is developed. The method is based on adaptation to image brightness variations, i.e., on the definition of quasisteady regions and computation of estimations in each region using approximation by the first-order surface by the two-dimensional method of least squares.

Quantitative Analysis of Feature Detection Using Adaptive Canny Edge Detector and Enhanced Ant Colony Optimization

Feature detection is a fundamental technique in broad fields of image processing, pattern recognition and computer vision. A digital image in general contains objects, edges, noises and background. Critical changes in properties of objects can be captured via detecting sharp variations in image brightness. The edges can be detected via numerous approaches on a basis of image intensity changes. Edge broken and false detection are typical problems using classical methods, which will result in information loss and feature deformity. The notion of optimization is thus introduced into edge detection. The Canny edge detector and Ant Colony Optimization (ACO) detector are among the most successful and effective approaches for edge detection. The Canny edge detector is designed to capture edges by searching local optima of the gradient of the intensity. It is susceptible to noises presenting on the raw images, so details of images could be slightly changed when Gaussian smoothing is applied. To improve accuracy, the adaptive edge tracing scheme is proposed. On the other hand, artificial intelligence has also been introduced. Being one of metaheuristic optimization approaches, the evolutionary computing oriented ACO becomes a promising approach for feature capturing without necessity of smoothing filters. Selection of maximum intensity difference as the path visibility function for ACO will contribute better to generate true edges and avoid false edges. Both the adaptive Canny edge detection and enhanced ACO are proposed in this article. Comparative studies are also conducted to evaluate the edge detection qualities. The outcomes are analyzed and evaluated from both qualitative and quantitative points of view, where merits and drawbacks of the two schemes have been indicated.

Automatic Optic Disc Detection From Retinal Images by a Line Operator

Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. A line operator is designed to capture such circular brightness structure, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and 81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained.

Retinal Vascular Fractal Dimension Measurement and Its Influence from Imaging Variation: Results of Two Segmentation Methods

Aim: To assess the influences of imaging variation (different photographic angle) on the measurement of retinal vascular fractal dimension (Df), using two segmentation methods.Materials and methods: Nonlinear orthogonal projection segmentation (International Retinal Imaging Software-Fractal, termed IRIS-Fractal) and curvature-based segmentation (Singapore Institute Vessel Assessment-Fractal, termed SIVA-Fractal) methods were used to measure Df and were assessed for their reproducibility in detecting retinal vessels of 30 stereoscopic pairs of optic disc color images. Each pair was taken from the same eye with slightly different angles of incidence. Each photograph of the pairs had subtle variations in brightness between areas temporal and nasal to the optic disc.Results: Intragrader reproducibility of Df measurement was similar (intraclass correlation 0.81 and 0.96, respectively) for IRIS-Fractal and SIVA-Fractal. Within-image pair Pearson’s correlation coefficients (r) of Df measurements were moderate for both methods (0.57 and 0.48, respectively).Conclusions: Both nonlinear orthogonal projection and curvature-based retinal vessel segmentation methods were found to be sensitive to variations in image brightness, resulting from iris shadowing associated with different angle of photographic incidence.

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.

Parallel four‐dimensional Haralick texture analysis for disk‐resident image datasets

AbstractTexture analysis is one possible method of detecting features in biomedical images. During texture analysis, texture‐related information is found by examining local variations in image brightness. Four‐dimensional (4D) Haralick texture analysis is a method that extracts local variations along space and time dimensions and represents them as a collection of 14 statistical parameters. However, application of the 4D Haralick method on large time‐dependent image datasets is hindered by data retrieval, computation, and memory requirements. This paper describes a parallel implementation using a distributed component‐based framework of 4D Haralick texture analysis on PC clusters. The experimental performance results show that good performance can be achieved for this application via combined use of task‐ and data‐parallelism. In addition, we show that our 4D texture analysis implementation can be used to classify imaged tissues. Copyright © 2006 John Wiley &amp; Sons, Ltd.

Direct recovery of planar-parallax from multiple frames

We present an algorithm that estimates dense planar-parallax motion from multiple uncalibrated views of a 3D scene. This generalizes the "plane+parallax" recovery methods to more than two frames. The parallax motion of pixels across multiple frames (relative to a planar surface) is related to the 3D scene structure and the camera epipoles. The parallax field, the epipoles, and the 3D scene structure are estimated directly from image brightness variations across multiple frames, without precomputing correspondences.

A Synthesis of Target-Localizing Algorithms Tolerant to Image Brightness Variations in Two-dimensional Correlative Extremal Navigation Systems Part II. Choosing a Primary Standard of Maximal Correlation and Optimal Algorithm Synthesis

A Synthesis of Target-Localizing Algorithms Tolerant to Image Brightness Variations in Two-dimensional Correlative Extremal Navigation Systems Part II. Choosing a Primary Standard of Maximal Correlation and Optimal Algorithm Synthesis

A Synthesis of Target-Localizing Algorithms Tolerant to Image Brightness Variations in Two-dimensional Correlative Extremal Navigation Systems Part I. The Formalization of the Problem on the Basis of the Grenander Pattern Theory

A Synthesis of Target-Localizing Algorithms Tolerant to Image Brightness Variations in Two-dimensional Correlative Extremal Navigation Systems Part I. The Formalization of the Problem on the Basis of the Grenander Pattern Theory

Image brightness variation of line-scan imaging systems using an alternating-current-operated light source for object illumination

We develop a theoretical model of image brightness variation of line-scan imaging systems using an ac-operated light source for object illumination. First, we derive an equation that relates the lamp operating frequency F, the object transport speed v, the image resolution R0 , and the number nc of lamp operating cycles within the time interval of one scan line. It is shown that nc5F/vR05Ftexp , where t exp51/vR0 is the exposure time of an image sensor element and is the time interval within one scan line. Following this, we define a normalized modulation function M(nc) that relates to the variation DE of the light energy E5*I(t )d t received by a sensor element over t exp , where I(t) is the intensity of object illumination. Three types of periodic functions for I(t) are consid- ered for which closed-form solutions for M(nc) can be obtained: square- wave, sinusoidal, and triangular-wave. For the square-wave function we show that M(nc) is a periodic function of nc , with zeros occurring at integral values of nc and peaks occurring at odd multiples of 1/2, with the heights of these peaks decreasing as 1/2nc with increasing nc . For the sinusoidal and triangular-wave functions, M(nc) has similar periodic be- havior except that the heights of the peaks decrease as 1/pnc and 1/4nc respectively. These results indicate that the square-wave function can be used to represent the worst case for assessing the performance of an imaging system under different conditions. Accordingly, we show nu- merically that lamps operating at a low frequency F550 Hz cannot be used for object illumination because the variation in image brightness is too large, and lamps operating at F520 kHz are generally applicable except at high object transport speed, typically at 100 in./s. Finally, we point out that this theoretical model may also be applied to area-scan imaging systems, such as for video cameras. For these imaging sys- tems, the normalized modulation function can be used to assess the variation of image brightness between one whole image and another, rather than between one scan line and another. © 1998 Society of Photo- Optical Instrumentation Engineers. (S0091-3286(98)03305-4)

A quantitative fluorescence-imaging technique for studying acetylcholine receptor turnover at neuromuscular junctions in living animals

We have developed a technique to measure changes in the amount of fluorescently labeled acetylcholine receptors in living muscles over long time periods. The measurements of fluorescence are made relative to a novel, photolytically stable fluorescence standard (Spectralon) which allows changes in fluorescence to be followed over days, even months. The method compensates for spatial and temporal variations in image brightness due to the light source, microscope, and camera. We use this approach to study the turnover of fluorescently labeled acetylcholine receptors at a single neuromuscular junction in a living mouse by re-imaging the same junction in situ over a period of 3 weeks. In addition we show that the SIT video camera, which is generally considered inadequate for quantitative imaging (in comparison to CCD cameras), is actually a very good quantitative device, especially in situations requiring both fast acquisition and high resolution.

Toward estimation of H[SUB]0[/SUB] from VLBI observations of the gravitational lens system 0957+561

We have analyzed two epochs of 6 cm very long base interferometry (VLBI) observations of the two brightest images, A and B, of the gravitationally lensed quasar 0957+561 in a search for correlated structural evolution from which to estimate delta tau(sub BA), the difference in propagation time for light traveling between the quasar and Earth along the paths corresponding to these two images. However, we detected only marginal increases in the separation between the core and the innermost jet component in each image over the 2 year interval 1987-1989. Because monitoring image brightness variations has since led to apparently reliable estimates of delta tau(sub BA), we intend to use spatial variations in the relative magnification field across the extent of the images to constrain further the mass distribution of the lens, which is now the principal contributor to the uncertainty in estimates of H(sub 0) from this lens system.

A new approach to study cardiac motion: The optical flow of cine MR images

We present a novel approach to study the intricate motion of the heart using the optical flow technique applied to cine MR images. The method uses image brightness variations between consecutive frames to compute in-plane displacements or velocities of moving image features. The dense velocity field thereby obtained throughout the myocardial wall allows to not only characterize segmental left ventricular wall motion but also to resolve "fine" motion such as intramural differences in displacements and therefore in thickening. Other subtle features of systole like the descent of the base towards the apex or the counterclockwise rotation of the apex with respect to the base can also be detected by the algorithm. Contrary to other techniques proposed earlier, this noninvasive method presents the additional advantages of not requiring any special pulse sequence nor well defined endocardial and epicardial outlines.

Development of a Multispectral Video System and its Application in Forestry

A 4-camera aerial multispectral video system was developed and evaluated in forest and land cover classification. The system is a low-cost alternative to aerial photographic and other electro-optical scanning systems. It is capable of imaging in narrow spectral bands in the visible and near IR. Both black-and-white band-sequential imagery and colour/false colour imagery are generated. In classification of land cover types, several classifiers and radiometric image processing procedures were evaluated to determine potential improvements in classification accuracy. Results showed that accurate classification of stand types and uniform cover types was possible if the data were processed to reduce noise, reduce spatial variations in image brightness due to optical and bi-directional reflectance effects, and add image texture information.

Enhancement of detectability by the use of coloured illumination for CT display.

Detectability of low-contrast lesions by computed tomography (CT) has been shown to depend on a number of observer-related factors, while remaining independent of others. Increasing image contrast, either by adjusting the video monitor or by reducing the display window width, enhances detectability by overcoming the effects of internal noise in the human visual system (Warren, 1981; Warren & Pandya, 1982). Increasing viewing distance causes an increase in detectability up to an optimal distance, followed by a reduction at still larger distances (Warren & Pandya, 1982). Moderate variations in image brightness produced by altering either the image illumination or the display window level have no significant effect on detectability (Warren, 1984). Similarly, detectability has been shown to be independent of variations in ambient illumination, from complete darkness to a moderately lit viewing-room (Simpson & Warren, unpublished observations). In this study we have investigated the effect of coloured illumination on the detectability of both natural and artificial low-contrast signals immersed in CT noise. Enhancement of detectability relative to white light was found to be related to variations in visual acuity at different wavelengths. These results, together with previously published observations, suggest that an increase of detectability by changing viewing conditions can usually be achieved only at the expense of a reduction in resolution.