Image Intensity Distribution Research Articles

Image Binarization using Intensity Range of Grayscale Images

The performance of binarization algorithms is determined by the selection of threshold value for binarization, and most of the previous binarization algorithms analyze the intensity distribution of the original images by using the histogram and determine the threshold value using the mean value of intensity or the intensity value corresponding to the valley of the histogram. The previous algorithms could not get the proper threshold value in the case that doesn't show the bimodal characteristic in the intensity histogram or for the case that tries to separate the feature area from the original image. Therefore, this paper proposed a novel algorithm for image binarization, which, first segments the intensity range of grayscale images to several intervals and calculates a mean value of intensity for each interval, and then repeats the interval integration until getting the final threshold value. The interval integration of two neighborhood intervals calculates the ratio of the distances between mean value and adjacent boundary value of two intervals and determine as the threshold value of the new integrated interval the intensity value that divides the distance between mean values of two intervals according to the ratio. The experiment for performance evaluation showed that the proposed algorithm generates the more effective threshold value than the previous algorithms.

High Vertical Resolution Full-Field Reflection-Type Three-Dimensional Angle-Deviation Microscope with Nonlinear Error Compensation

This study examines the use of reflectivity-height transformation in full-field angle-deviation microscopes (ADM). In such microscopes, two light intensity distribution images of a prism's total internal reflection and critical angle are obtained separately with two charge-coupled devices (CCDs), and are converted into a reflectivity profile point-to-point and then into angle of deviation matrix after the beam is reflected by the test sample; finally, the surface height of the sample is found through the triangular geometrical relationship. This method obtains the image through the effective imaging area of CCD. Once the two-dimensional (2D) image is obtained, the third dimension, height, is added to create a full-field 3D surface profile. Its conversion process is nonlinear; therefore, compensation must be made to reduce measurement errors. The optical magnification of high vertical resolution full-field 3D reflection-type ADM could reach >250 times, thus providing submicron measurements with nanometer vertical resolution and allowing for the simultaneous measurement of 2D and 3D images. Small defects on both transparent and nontransparent surfaces can be rapidly detected.

Phase shifting masks in Displacement Talbot Lithography for printing nano-grids and periodic motifs

High-resolution periodic structures with geometric shapes such as squares or triangles cannot be readily produced with low-cost photolithography methods like interference or proximity photolithography. We present a method for robust printing of such patterns with Displacement Talbot Lithography (DTL) using phase shifting masks. This combination effectively yields high-contrast images that extend to large distances from the mask. It therefore enables uniform images to be printed over large, non-flat substrates without loss of resolution. Phase masks with several different motifs have been designed, fabricated and tested using DTL exposures. High-fidelity grid and island type structures with square and triangular motifs and sub-micron feature sizes were printed into different types of photoresists using wafer–mask gaps on the order of 100μm. The experimental results show good agreement with calculated image intensity distributions. The method is suitable for low-cost production of high-quality patterned growth substrates and photonic structures among other applications.

Forming Freeform Source Shapes by Utilizing Particle Swarm Optimization to Enhance Resolution in Extreme UV Nanolithography

This paper investigated pixel-based source shape optimization to enhance the resolution in a projection extreme UV (EUV) lithography system. The particle swarm optimization method was proposed to optimize the source shapes. The layout patterns of the masks were corrected using model-based optical proximity correction. The optimal source shape in an EUV lithography system was verified using numerical calculations, through which the aerial image of two types of mask pattern exposure was determined. The two types of mask patterns were a logic line/space (L/S) pattern with a critical dimension (CD) of 16 nm and a SRAM contact hole (CH) pattern with a CD of 45 nm. Two significant metrics were used to evaluate the modified source performance: the latent image intensity and process window. Results from the numerical calculations showed that for L/S target patterns, using the optimal source produced a latent image error of 13.02% after exposure that was superior to that of traditional off-axis source exposure. The latent image intensity of the mask between the lines and the gaps differed substantially, corner rounding situations had effectively improved, and no bridges were observed. Concerning CH target patterns, using the optimal source produced a latent image error of 0.04% after exposure that was also superior to that of traditional sources. The latent image intensity between the holes and the gaps also differed significantly, and the latent image intensity distribution at the four corners of the CH had straight angles. Because the source shapes calculated in this study comprised pixel-based sources, the controllable microelectromechanical system mirror array chip was used to match each mirror to each pixel source, and the calculated source shapes were successfully projected.

Impacts of right ventricular trabeculae and papillary muscles on volumes and function assessed by cardiovascular magnetic resonance using a novel software: semi-automatic threshold-based segmentation algorithm

Background The objective of this study was to assess the impact of right ventricular (RV) trabeculae and papillary muscles on measured volumes and function assessed by cardiovascular magnetic resonance (CMR) using a novel semiautomatic segmentation algorithm in patients with right heart lesion of the congenital heart diseases. The new algorithm was based on the signal intensity distribution of MR images, and excludes trabeculae and papillary muscles from the blood pool, while the manual approach includes these objects in the blood pool.

Reduction in Edge-Ringing in Aberrated Images of Coherent Edge Objects by Multishaded Aperture

The images of a straight edge in coherent illumination produced by an optical system with circular aperture and apodized with multiple filters have been studied. The most common problem encountered in the coherent-imaging techniques is the edge-ringing. To minimize the edge-ringing, multishaded aperture method has been proposed. Image intensity distribution curves are drawn and edge-ringing values are evaluated. The results are compared to that of the airy case with the use of single, double and triple filtering.

An efficient Self-Organizing Active Contour model for image segmentation

Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, based on the evolution of an active contour. Among ACMs, supervised ACMs are able to exploit the information extracted from supervised examples to guide the contour evolution. However, their applicability is limited by the accuracy of the probability models they use. As a consequence, effectiveness and efficiency of supervised ACMs are among their main real challenges, especially when handling images containing regions characterized by intensity inhomogeneity. In this paper, to deal with such kinds of images, we propose a new supervised ACM, named Self-Organizing Active Contour (SOAC) model, which combines a variational level set method (a specific kind of ACM) with the weights of the neurons of two Self-Organizing Maps (SOMs). Its main contribution is the development of a new ACM energy functional optimized in such a way that the topological structure of the underlying image intensity distribution is preserved - using the two SOMs - in a parallel-processing and local way. The model has a supervised component since training pixels associated with different regions are assigned to different SOMs. Experimental results show the superior efficiency and effectiveness of SOAC versus several existing ACMs.

Anomalous enhancement in interfacial perpendicular magnetic anisotropy through uphill diffusion.

We observed interfacial chemical sharpening due to uphill diffusion in post annealed ultrathin multilayer stack of Co and Pt, which leads to enhanced interfacial perpendicular magnetic anisotropy (PMA). This is surprising as these elements are considered as perfectly miscible. This chemical sharpening was confirmed through quantitative energy dispersive x-ray (EDX) spectroscopy and intensity distribution of images taken on high angle annular dark field (HAADF) detector in Scanning Transmission Electron Microscopic (STEM) mode. This observation demonstrates an evidence of miscibility gap in ultrathin coherent Co/Pt multilayer stacks.

Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography

Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic lesions, is of fundamental importance for patient management. Image intensity-based textural features have been recognized as useful biomarker for the differentiation task. In this paper, we introduce texture features from higher-order images, i.e., gradient and curvature images, beyond the intensity image, for that task. Based on the Haralick texture analysis method, we introduce a virtual pathological model to explore the utility of texture features from high-order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on a database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the support vector machine classifier and the merit of area under the curve (AUC) of the receiver operating characteristics. The AUC of classification was improved from 0.74 (using the image intensity alone) to 0.85 (by also considering the gradient and curvature images) in differentiating the neoplastic lesions from non-neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas. The experimental results demonstrated that texture features from higher-order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography colonography for colorectal cancer screening by not only detecting polyps but also classifying them for optimal polyp management for the best outcome in personalized medicine.

Aberration measurement technique based on an analytical linear model of a through-focus aerial image

We propose an in situ aberration measurement technique based on an analytical linear model of through-focus aerial images. The aberrations are retrieved from aerial images of six isolated space patterns, which have the same width but different orientations. The imaging formulas of the space patterns are investigated and simplified, and then an analytical linear relationship between the aerial image intensity distributions and the Zernike coefficients is established. The linear relationship is composed of linear fitting matrices and rotation matrices, which can be calculated numerically in advance and utilized to retrieve Zernike coefficients. Numerical simulations using the lithography simulators PROLITH and Dr.LiTHO demonstrate that the proposed method can measure wavefront aberrations up to Z(37). Experiments on a real lithography tool confirm that our method can monitor lens aberration offset with an accuracy of 0.7 nm.

Fabrication and characterization of Ge20Sb15S65 chalcogenide glass for photonic crystal fibers

Chalcogenide glasses are known for their high transparency in the mid-infrared (IR) range, which includes two atmospheric windows that lie from 3 to 5 μm and 8 to 12 μm, respectively. Chalcogenide photonic crystal fibers have numerous potential applications in the field of IR, such as spectroscopy, microscopy, astronomy, biology, and sensing. In this paper, Ge20Sb15S65 chalcogenide glass was fabricated and systematically studied. Chalcogenide glass has high transmission property (>70 %), good thermal stability, and good mechanical stability. The glass transition temperature Tg is 296 °C, and no exothermic peak was associated with crystallization up to 500 °C, which indicates its suitability for fiber drawing. As a result of its excellent mechanical properties, preforms with a variety of geometrical patterns were fabricated by using mechanical drilling. The near-field intensity distribution image of the drawn fiber shows a strong light propagation confinement.

Quantification of osteoarticular joint defects through bone segmentation and modeling

Shoulder instability is a major threat to people's daily life. Many patients suffer from shoulder instability such as the loss of the glenoid and humeral head. In clinical practice, an accurate 3D structure estimation of damaged joints is necessary to diagnose and treat bone defects. This study quantifies osteoarticular defects through the modeling and visualization of osteoarticular structures. An improved algorithm to extract the 3D structure of the bones is proposed. The bone contour is then automatically extracted using prior shape and gray scale intensity distribution of joint CT images. Joint structures with mirror symmetry are matched using the Iterative Closest Point registration algorithm. Osteoarticular defects can be quantified on the basis of the symmetric information of the bones. Experimental results demonstrate that the proposed method can effectively segment the joint structures from the CT image. In addition, the proposed mirror symmetrical method can effectively estimate osteoarticular defects.

Combining learning-based intensity distributions with nonparametric shape priors for image segmentation

Integration of shape prior information into level set formulations has led to great improvements in image segmentation in the presence of missing information, occlusion, and noise. However, most shape-based segmentation techniques incorporate image intensity through simplistic data terms. A common underlying assumption of such data terms is that the foreground and the background regions in the image are homogeneous, i.e., intensities are piecewise constant or piecewise smooth. This situation makes integration of shape priors inefficient in the presence of intensity inhomogeneities. In this paper, we propose a new approach for combining information from shape priors with that from image intensities. More specifically, our approach uses shape priors learned by nonparametric density estimation and incorporates image intensity distributions learned in a supervised manner. Such a combination has not been used in previous work. Sample image patches are used to learn the intensity distributions, and segmented training shapes are used to learn the shape priors. We present an active contour algorithm that takes these learned densities into account for image segmentation. Our experiments on synthetic and real images demonstrate the robustness of the proposed approach to complicated intensity distributions, and occlusions, as well as the improvements it provides over existing methods.

Context affects lightness at the level of surfaces

The accurate perception of object attributes such as surface lightness is vital for the successful interaction with the environment. However, it is unknown how the visual system assigns lightness values to surfaces based on the intensity distribution in the retinal image. It has been shown that the perceived lightness of an image region is influenced by its context but whether that influence involves so-called low-, mid- or high-level mechanisms is disputed. To probe the level of lightness perception psychophysically we manipulated the surface character of a target region and measured its influence on perceived lightness. We show that only when the target region was consistent with the perceptual interpretation of a surface it was subject to a subjective brightening (assimilation) effect. When the target region was consistent with a spotlight, and hence inconsistent with surface perception, there was no concomitant increase in perceived lightness. These results suggest that the effect of context on the lightness of an image region is not deterministic, but can instead be modulated by other attributes of the image region that imply a mid-level scene interpretation.

Study on Pretreatment Method of X-Ray Real-Time Imaging Digital Image

X-ray nondestructive testing has a wide range of applications, which in materials testing, food testing, manufacturing, instrumentation, automotive parts and other fields having good performance. The paper mainly deals with low contrast X-ray digital images, image edge blur features and digital image preprocessing techniques of contrast. By a crack image taking geometric transformations, gray-scale transformations and image enhancement processing such as pretreatment technology airspace transforms, getting three options that have been able to effectively realize image denoising and enhancement. These three sets of processing solutions, to some extened, opening the image intensity distribution and making cracks sharper image segmentation is the foundation of subsequentence.

Efficient Contrast Enhancement using Kernel Padding and DWT with Image Fusion

enhancement algorithms for varying intensity distribution Images creates intensity distortion in some regions, over enhancement & unnatural effects in other regions of Images. The main reason of this effect is due to not consideration of image edges & sharp details during enhancement process. On the other hand, the human visual system is more sensitive to edges and sharp details of image. In this paper we are proposing a method titled efficient contrast Enhancement using Kernel Padding and DWT with Image Fusion that Enhances the contrast of Images that has varying intensity distribution specially satellite images, preserve the brightness of images, sharpens the edges and remove the blurriness of images. Basically this is a pixel based edge guided image fusion technique. In this method LL sub band of Image DWT is processed by contrast enhancement section where based on image brightness level image is decomposed in different layers and then each layers intensity is stressed or compressed by generated intensity transformation function. The decomposed intensity layers are also processed by canny edge detection method as all the satellite images contains the noise due to atmospheric turbulence and this is Gaussian by nature. Canny edge detector is the best method for detecting edges of image in the presence of Gaussian noise. Finally the contrast enhanced images are fused according to the weight map determined by edge map of image.

Localized field enhancement at metallic subwavelength structures

The near field intensity distribution and the near-field transmission spectrums of metallic nanostructures simulated by the finite-difference time-domain method are present in the text. Better field intensity localization and larger field intensity enhancement exist in the near field intensity distribution images for nanoparticle array in the near-infrared wavelength range. The minimum transmission intensity collected at the metal film surface can exceed 1.0 and the electric field intensity can limit in a number of nanometer. Meanwhile, these nanoparticle array structures can extensively apply to biochemical sensor devices design and so on.

Improvement of the Focusing Resolution of Photonic Crystal Negative Refraction Imaging with a Hollow Component Structure

The negative refraction and imaging effects in photonic crystals can be used to solve diffraction limit problem in near-field optics. Improving transmission efficiency and image resolution is a critical work for negative refraction imaging. We theoretically investigate the band structures, equi-frequency surfaces, electromagnetic wave propagation, and the image intensity distributions in a two-dimensional hexagonal photonic crystal consisting of hollow components. It is found that, in contrast to a hexagonal photonic crystal consisting of solid dielectric cylinders of the same radius, photonic crystals with hollow components can be used to optimize the all-angle negative refraction. Numerical simulations show that the transmission efficiency and resolution of image can be enhanced by changing the radii of the hollow air rods.

Performance of the Moving Voxel Image Reconstruction (MVIR) Method in the Fixed Site Detection System (FSDS) Prototype

We have developed a dynamic gamma-ray emission image reconstruction method called MVIR (Moving Voxel Image Reconstruction) for lane detection in multilane portal monitor systems. MVIR was evaluated for use in the Fixed Site Detection System (FSDS), a prototype three-lane gamma-ray portal monitor system for EZ-pass toll plazas. As a baseline, we compared MVIR with a static emission image reconstruction method in analyzing the same real and simulated data sets. Performance was judged by the distributions of image intensities for source and no-source vehicles over many trials as a function of source strength. We found that MVIR produced significantly better results in all cases. The performance difference was greatest at low count rates, where source/no-source distributions were well separated with the MVIR method, allowing reliable source vehicle identification with a low probability of false positive identifications. Static emission image reconstruction of the same data produced overlapping distributions that made source vehicle identification unreliable. The performance of the static method was acceptable at high count rates. Both algorithms reliably identified two strong sources passing through at nearly the same time.

Cortical surface reconstruction via unified Reeb analysis of geometric and topological outliers in magnetic resonance images.

In this paper we present a novel system for the automated reconstruction of cortical surfaces from T1-weighted magnetic resonance images. At the core of our system is a unified Reeb analysis framework for the detection and removal of geometric and topological outliers on tissue boundaries. Using intrinsic Reeb analysis, our system can pinpoint the location of spurious branches and topological outliers, and correct them with localized filtering using information from both image intensity distributions and geometric regularity. In this system, we have also developed enhanced tissue classification with Hessian features for improved robustness to image inhomogeneity, and adaptive interpolation to achieve sub-voxel accuracy in reconstructed surfaces. By integrating these novel developments, we have a system that can automatically reconstruct cortical surfaces with improved quality and dramatically reduced computational cost as compared with the popular FreeSurfer software. In our experiments, we demonstrate on 40 simulated MR images and the MR images of 200 subjects from two databases: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and International Consortium of Brain Mapping (ICBM), the robustness of our method in large scale studies. In comparisons with FreeSurfer, we show that our system is able to generate surfaces that better represent cortical anatomy and produce thickness features with higher statistical power in population studies.