Variations In Image Intensity Research Articles

Brain MR image normalization in texture analysis of multiple sclerosis

A problem that occurs in quantitative texture analysis of magnetic resonance imaging (MRI) is that there are intra-scan and inter-scan image intensity variations due to the MRI instrumentation. Therefore, image intensity normalization methods should be applied prior to further image analysis. The objective of this work was to investigate six previously described MRI intensity normalization methods and propose the most appropriate for the pre-processing of brain T2-weighted MR images acquired from 38 symptomatic untreated multiple sclerosis (MS) subjects. The following normalization methods were investigated: Contrast Stretch Normalization (CSN), Intensity Scaling (IS), Histogram Stretching (HS), Histogram Normalization (HN), Gaussian Kernel Normalization (GKN), and Histogram Equalization (HE). The main findings of this study can be summarized as follows: 1) Lesion texture features were affected differently by the normalization process for both the 0 and 6-12 months MRI scans. For example, for the features median and contrast there was significant difference between 0 and 6-12 months for the original MRI images but not for the HN normalized ones. On the other hand for the feature complexity there was no significant difference between 0 and 6-12 months for the original MRI images, but there was for the HN normalized images. 2) The statistical lesion feature analysis between the original and the normalized images showed that the HN method gave the highest number of significant features after normalization for both the 0 and 6-12 months MRI scans. 3) The HN normalization method gave the best performance compared to the other normalization methods with respect to the distance measures, structural similarity index, coefficient of variation, and correlation coefficient between the original and the normalized 0 and 6-12 months MRI scans. Thus, based on the above findings it is recommended that the simple HN normalization method could be used prior to quantitative texture analysis in the case study presented. The findings of this study provide evidence that texture features of MRI-detectable brain white matter lesions may have an additional potential role in the clinical evaluation of MRI images in MS. This is a prerequisite step in the assessment of texture features as surrogate markers of disease progression. However, a larger scale study is needed to establish the application in clinical practice and for computing shape parameters and texture features that may provide information for better and earlier differentiation between normal tissue and MS lesions.

Color image trend adjustment using a color bidimensional empirical mode decomposition method

Bidimensional empirical mode decomposition (BEMD) technique decomposes an image into several bidimensional intrinsic mode functions (BIMFs) and a bidimensional residue (BR). Classical BEMD methods require some form of surface interpolation to estimate envelope surfaces, which causes various problems. On the other hand, existing surface interpolation-based BEMD methods are proposed and/or are suitable for gray-scale images only. This paper presents a novel BEMD approach for color images known as color BEMD (CBEMD), which employs order-statistics filter (OSF)-based envelope estimation to avoid some of the difficulties, otherwise encountered in classical BEMD approaches. The CBEMD can decompose a color image into several color BIMFs and a color BR based on hierarchical local spatial variation of image intensity and color. Since the color BR represents the trend of a color image in terms of global intensity and color distribution, it is utilized for trend adjustment of color images. Both formulation of the CBEMD and finding and adjusting the color image trend are known to be the first approaches in corresponding issues. Experimental results with several real images demonstrate the potential of the proposed CBEMD method for color image processing, which include trend adjustment of color images.

Nonlocal regularization for active appearance model: Application to medial temporal lobe segmentation

The human medial temporal lobe (MTL) is an important part of the limbic system, and its substructures play key roles in learning, memory, and neurodegeneration. The MTL includes the hippocampus (HC), amygdala (AG), parahippocampal cortex (PHC), entorhinal cortex, and perirhinal cortex--structures that are complex in shape and have low between-structure intensity contrast, making them difficult to segment manually in magnetic resonance images. This article presents a new segmentation method that combines active appearance modeling and patch-based local refinement to automatically segment specific substructures of the MTL including HC, AG, PHC, and entorhinal/perirhinal cortex from MRI data. Appearance modeling, relying on eigen-decomposition to analyze statistical variations in image intensity and shape information in study population, is used to capture global shape characteristics of each structure of interest with a generative model. Patch-based local refinement, using nonlocal means to compare the image local intensity properties, is applied to locally refine the segmentation results along the structure borders to improve structure delimitation. In this manner, nonlocal regularization and global shape constraints could allow more accurate segmentations of structures. Validation experiments against manually defined labels demonstrate that this new segmentation method is computationally efficient, robust, and accurate. In a leave-one-out validation on 54 normal young adults, the method yielded a mean Dice κ of 0.87 for the HC, 0.81 for the AG, 0.73 for the anterior parts of the parahippocampal gyrus (entorhinal and perirhinal cortex), and 0.73 for the posterior parahippocampal gyrus.

Laplace-distributed increments, the Laplace prior, and edge-preserving regularization

Abstract. For a given two-dimensional image, we define the horizontal and vertical increments at a pixel location to be the difference between the intensity values at that pixel and at the neighboring pixels to the right and above, respectively. For a typical image, it makes intuitive sense that the increments will usually be near zero, corresponding to areas of smooth variation in image intensity, but will often have large magnitude, corresponding to edges where sharp intensity changes occur. In this paper, we explore the use of the Laplace increment model, in which the increments are assumed to be independent and identically distributed Laplace random variables – a distribution with heavy tails allowing for large increment values – with zero mean. The prior constructed from the Laplace increment model is very similar to the total variation (TV) prior. We perform a theoretical analysis of its properties, which shows that the Laplace prior yields a regularization scheme with regularized solutions contained in the space of bounded variation, just as for the TV prior. Moreover, numerical experiments indicate that the Laplace prior yields reconstructions that are qualitatively very similar to those obtained using TV.

Optimization of electromagnetic fields in holographic lithography using the method of local variations

The problem of optimization of the Gabor hologram for creating light images with required intensity distribution is considered. The amplitude of the image intensity variation is decreased to technologically acceptable values as a result of optimization of a computer synthesized hologram, which essentially reduced the "Gibbs effect", the distortions occurring at the edges of the created object in the case of physical hologram recording. The results of optimization of electromagnetic fields using the method of local variations are demonstrated.

Computer simulation study of nonlinear imaging properties for two phase-typed scatterers

Nonlinear imaging of two phase-typed scatterers in super-Gaussian laser beams is modeled and its properties obtained by computer simulation are presented. The formations of hot images and second-order hot images are verified. It is found that the in-beam locations of hot images correspond to those of the scatterers, but that there can be only one second-order hot image, which is at the middle point between the in-beam locations of the scatterers. Interestingly, the image intensity can be suppressed and it increases in an oscillating manner with some regularity as the distance between the scatterers increases. Moreover, one more scatterer makes the effect of the B integral and that of the phase shift caused by scatterers quite different from the predictions for single-scatterer case. The variation trend of hot image intensity with the B integral is not in agreement with that described by the analytical result for the single-scatterer case. The variation of phase shift caused by the scatterers can result in two peaks in the variation of hot image intensity with it, and the phase shift corresponding to the larger peak is approximately half the predicted result for the single-scatterer case. These results indicate that the number of scatterers has a significant influence on nonlinear imaging.

다수의 고유 공간을 이용한 주화 표면 품질 진단

In a manufacturing process of metal coins, surface defects of coins are manually detected. This paper describes an new method for detecting surface defects of metal coins on a moving conveyor belt using image processing. This method consists of multiple procedures: segmentation of a coin from the background, alignment of the coin to the model, projection of the aligned coin to the best eigen image space, and detection of defects by comparison of the projection error with an adaptive threshold. In these procedures, the alignement and the projection are newly developed in this paper for the detection of coin surface defects. For alignment, we use the histogram of the segmented coin, which converts two-dimensional image alignment to one-dimensional alignment. The projection reduces the intensity variation of the coin image caused by illumination and coin rotation change. For projection, we build multiple eigen image spaces and choose the best eigen space using estimated coin direction. Since each eigen space consists of a small number of eigen image vectors, we can implement the projection in real- time.

Linear-nonlinear neuronal model for shape from shading

The goal of shape from shading (SFS) is to recover a relative depth map from the variations of image intensity associated to changes in surface shape. There have been very few attempts at developing biologically plausible solutions to this problem, and a sound neurophysiological basis is still missing. Here we present a biologically inspired approach to SFS, formulated in terms of the well-known linear-nonlinear model of neuronal responses. Without resorting to the image irradiance equation, which is at the heart of the traditional SFS algorithms, we submit the input image to a linear filter followed by nonlinear transformations modelled on the tuning curves of the disparity-selective binocular neurons. This yields plausible shape estimates, without requiring information regarding surface reflectance or illumination.

Cyclic CINE‐balanced steady‐state free precession image intensity variations: Implications for the detection of myocardial edema

To evaluate the dependence of CINE-balanced steady-state free precession (bSSFP) image intensities on spatial location, cardiac phase, and disease state. Eight subjects with recent myocardial infarctions and eight age- and sex-matched normal volunteers were studied using CINE-bSSFP imaging to describe cyclic image intensity variations as a function of the cardiac cycle and to optimize and assess the ability of CINE-bSSFP imaging to depict myocardial edema. Signal intensities of the left ventricular (LV) bloodpool and myocardium were measured using region-of-interest analysis across the cardiac cycle. The magnitude and time course of the cyclic variations were evaluated. Mixed-model analysis of variance was used to examine the influence of physical location, cardiac phase, and presence of myocardial infarction. The LV bloodpool and myocardial CINE-bSSFP signal intensities varied significantly with spatial location, cardiac phase, and disease (P < 0.001). Cardiac phase had a significant effect on the signal intensities after adjustments for spatial location. The LV bloodpool signal decreased slowly during systole and rose sharply during LV filling. There were two distinct myocardial intensity peaks, one occurring at peak systole and the other at the end of the LV rapid inflow phase. Myocardial edema was seen as a hyperintense region. Image contrast with adjacent myocardium was the greatest at the end of systole. Detection of myocardial edema using the conventional CINE-bSSFP technique is feasible, but is complicated by normal cyclic changes in myocardial image intensities during the cardiac cycle.

Vibro-acoustography and multifrequency image compounding

Vibro-acoustography is an ultrasound based imaging modality that can visualize normal and abnormal soft tissue through mapping the acoustic response of the object to a harmonic radiation force at frequency Δ f induced by focused ultrasound. In this method, the ultrasound energy is converted from high ultrasound frequencies to a low acoustic frequency (acoustic emission) that is often two orders of magnitude smaller than the ultrasound frequency. The acoustic emission is normally detected by a hydrophone. Depending on the setup, this low frequency sound may reverberate by object boundaries or other structures present in the acoustic paths before it reaches the hydrophone. This effect produces an artifact in the image in the form of gradual variations in image intensity that may compromise image quality. The use of tonebursts with finite length yields acoustic emission at Δ f and at sidebands centered about Δ f. Multiple images are formed by selectively applying bandpass filters on the acoustic emission at Δ f and the associated sidebands. The data at these multiple frequencies are compounded through both coherent and incoherent processes to reduce the acoustic emission reverberation artifacts. Experimental results from a urethane breast phantom are described. The coherent and incoherent compounding of multifrequency data show, both qualitatively and quantitatively, the efficacy of this reverberation reduction method. This paper presents theory describing the physical origin of this artifact and use of image data created using multifrequency vibro-acoustography for reducing reverberation artifacts.

Automatic Best Reference Slice Selection for Smooth Volume Reconstruction of a Mouse Brain From Histological Images

In this paper, we present a novel and effective method for registering histological slices of a mouse brain to reconstruct a 3-D volume. First, intensity variations in images are corrected through an intensity standardization process so that intensity values remain constant across slices. Second, the image space is transformed to a feature space where continuous variables are taken as high fidelity image features for accurate registration. Third, in order to improve the quality of the reconstructed volume, an automatic best reference slice selection algorithm is developed based on iterative assessment of image entropy and mean square error of the registration process. Fourth, a novel metric for evaluating the quality of the reconstructed volume is developed. Finally, the effect of optimal reference slice selection on the quality of registration and subsequent reconstruction is demonstrated.

Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking

BackgroundSegmentation of the coronary angiogram is important in computer-assisted artery motion analysis or reconstruction of 3D vascular structures from a single-plan or biplane angiographic system. Developing fully automated and accurate vessel segmentation algorithms is highly challenging, especially when extracting vascular structures with large variations in image intensities and noise, as well as with variable cross-sections or vascular lesions.MethodsThis paper presents a novel tracking method for automatic segmentation of the coronary artery tree in X-ray angiographic images, based on probabilistic vessel tracking and fuzzy structure pattern inferring. The method is composed of two main steps: preprocessing and tracking. In preprocessing, multiscale Gabor filtering and Hessian matrix analysis were used to enhance and extract vessel features from the original angiographic image, leading to a vessel feature map as well as a vessel direction map. In tracking, a seed point was first automatically detected by analyzing the vessel feature map. Subsequently, two operators [e.g., a probabilistic tracking operator (PTO) and a vessel structure pattern detector (SPD)] worked together based on the detected seed point to extract vessel segments or branches one at a time. The local structure pattern was inferred by a multi-feature based fuzzy inferring function employed in the SPD. The identified structure pattern, such as crossing or bifurcation, was used to control the tracking process, for example, to keep tracking the current segment or start tracking a new one, depending on the detected pattern.ResultsBy appropriate integration of these advanced preprocessing and tracking steps, our tracking algorithm is able to extract both vessel axis lines and edge points, as well as measure the arterial diameters in various complicated cases. For example, it can walk across gaps along the longitudinal vessel direction, manage varying vessel curvatures, and adapt to varying vessel widths in situations with arterial stenoses and aneurysms.ConclusionsOur algorithm performs well in terms of robustness, automation, adaptability, and applicability. In particular, the successful development of two novel operators, namely, PTO and SPD, ensures the performance of our algorithm in vessel tracking.

Magnetic resonance cholangiopancreatography image enhancement for automatic disease detection

To sufficiently improve magnetic resonance cholangiopancreatography (MRCP) quality to enable reliable computer-aided diagnosis (CAD). A set of image enhancement strategies that included filters (i.e. Gaussian, median, Wiener and Perona-Malik), wavelets (i.e. contourlet, ridgelet and a non-orthogonal noise compensation implementation), graph-cut approaches using lazy-snapping and Phase Unwrapping MAxflow, and binary thresholding using a fixed threshold and dynamic thresholding via histogram analysis were implemented to overcome the adverse characteristics of MRCP images such as acquisition noise, artifacts, partial volume effect and large inter- and intra-patient image intensity variations, all of which pose problems in application development. Subjective evaluation of several popular pre-processing techniques was undertaken to improve the quality of the 2D MRCP images and enhance the detection of the significant biliary structures within them, with the purpose of biliary disease detection. The results varied as expected since each algorithm capitalized on different characteristics of the images. For denoising, the Perona-Malik and contourlet approaches were found to be the most suitable. In terms of extraction of the significant biliary structures and removal of background, the thresholding approaches performed well. The interactive scheme performed the best, especially by using the strengths of the graph-cut algorithm enhanced by user-friendly lazy-snapping for foreground and background marker selection. Tests show promising results for some techniques, but not others, as viable image enhancement modules for automatic CAD systems for biliary and liver diseases.

A solid iodinated phantom material for use in tomographic x‐ray imaging

Iodinated phantoms are of value in x-ray imaging for quality control measurements, system calibration, and for use in the research setting; however, the liquid phantoms that are most often used have several limitations including variability between repeated dilutions, inhomogeneities from air bubbles or precipitants, and long set up times. Although suitable materials have been investigated for projection radiography, quantitative measurements of contrast enhancement in computed tomography (CT) have become increasingly important in the clinic, and a need exists for a durable and reproducible iodinated phantom material. In this work, the authors describe a solid radiographic phantom material that has an accurately known concentration of iodine distributed uniformly throughout its volume and that has stable properties over time. This material can be molded or machined into a desired shape to create a test object or for use in an anthropomorphic phantom. Two sets of calibration phantoms were produced with a clinically relevant range of iodine concentrations. Measurements were made on these phantoms to characterize the material properties in terms of accuracy of iodine concentration, radiographic uniformity, temporal stability of x-ray attenuation, and manufacturing repeatability. Experimentally measured linear x-ray attenuation coefficients were compared to those predicted by a theoretical model. The uniformity of the iodine distribution in the material was assessed by measuring image intensity variation, both in projection images and in reconstructed CT volumes. The reproducibility of manufacture was estimated on a set of independently produced samples. A longitudinal study was performed to assess the stability of the material x-ray characteristics over time by making measurements at 6 month intervals. Good agreement was seen between the experimental measurements of effective attenuation and the theoretically predicted values. It is estimated that a desired iodine concentration could be produced to within 0.04 mg/ml. Comparison of the measured effective linear iodine attenuation coefficients of eight 1.0 mg/ml samples indicated a manufacturing reproducibility of +/-0.03 mg/ml iodine. Variations in uniformity across each of the samples were on the order of image intensity fluctuations (sigma). No inhomogeneities due to mixing or settling were apparent. An analysis of longitudinal data collected for both calibration sets revealed no perceptible change in radiographic properties over the first 6 months after manufacture, nor over a subsequent 1.5 yr period from 1 yr postmanufacture onward. The uniformity, stability, and precision of this iodinated material suggest that it is suitable for use in accurate calibration tools for contrast tomographic imaging.

Time-efficient fast spin echo imaging at 4.7 T with low refocusing angles

An implementation of fast spin echo at 4.7 T designed for versatile and time-efficient T(2)-weighted imaging of the human brain is presented. Reduced refocusing angles (alpha < 180 degrees) were employed to overcome specific absorption rate (SAR) constraints and their effects on image quality assessed. Image intensity and tissue contrast variations from heterogeneous RF transmit fields and incidental magnetization transfer effects were investigated at reduced refocusing angles. We found that intraslice signal variations are minimized with refocusing angles near 180 degrees, but apparent gray/white matter contrast is independent of refocusing angle. Incidental magnetization transfer effects from multislice acquisitions were shown to attenuate white matter intensity by 25% and gray matter intensity by 15% at 180 degrees; less than 5% attenuation was seen in all tissues at flip angles below 60 degrees. We present multislice images acquired without excess delay time for SAR mitigation using a variety of protocols. Subsecond half Fourier acquisition single-shot turbo spin echo (HASTE) images were obtained with a novel variable refocusing angle echo train (20 degrees < alpha < 58 degrees) and high-resolution scans with a voxel volume of 0.18 mm(3) were acquired in 6.5 min with refocusing angles of 100 degrees.

Assessment of weld thickness loss in offshore pipelines using computed radiography and computational modeling

In order to guarantee the structural integrity of oil plants it is crucial to monitor the amount of weld thickness loss in offshore pipelines. However, in spite of its relevance, this parameter is very difficult to determine, due to both the large diameter of most pipes and the complexity of the multi-variable system involved. In this study, a computational modeling based on Monte Carlo MCNPX code is combined with computed radiography to estimate the weld thickness loss in large-diameter offshore pipelines. Results show that computational modeling is a powerful tool to estimate intensity variations in radiographic images generated by weld thickness variations, and it can be combined with computed radiography to assess weld thickness loss in offshore and subsea pipelines.

SU‐GG‐I‐95: Simpler and Faster Watershed Medical Image Segmentation Algorithm

Purpose: A watershed medical image segmentation method has a drawback of producing a region for each local minimum, resulting in oversegmentation. To alleviate this problem, a post merging process such as a hybrid graph merging algorithm is used, which impedes interactive segmentation with a graphical user interface. An effective watershed algorithm without a post merging process is proposed. Method and Materials: Our algorithm, as the preprocessing, includes the edge‐preserving noise reduction and gradient‐based boundary sharpening steps. Because the initial oversegmentation depends on the gradient image intensity variations, the noise reduction algorithm can reduce the problem appreciably. A well‐known statistical edge‐preserving noise reduction algorithm is implemented to preserve the boundaries of the image objects which have different physiological properties. Gradient‐based boundary sharpening stage follows the novel edge‐preserving noise reduction step. Boundary sharpening process combined with watershed segmentation results in an effective watershed algorithm without a post merging process. The combination of the standard gradient values with two cross operators makes the edges sharper and thinner. A threshold value is controlled interactively with a steppedup graphical user interface in the boundary sharpening process. Results: We applied the method to both CT and MR images. Interactive real time medical image segmentation was possible with a graphical user interface. Threshold values were manipulated interactively with a mouse to get an appropriate segmentation of the abdomen CT image. It was also confirmed that this method produced the same segmentations as those of hybrid‐merging algorithm for the MR brain images. Conclusion: In the gradient‐based watershed, computing time is negligible and storage requirement can be reduced appreciably because a post hybrid merging algorithm is not necessary. Moreover, interactive real time segmentation is possible with the steppedup graphical user interface.

Radiofrequency Excitation

AbstractThis unit presents an in‐depth discussion of the radiofrequency field, including how variations in the RF field will cause variations in image intensity that are independent of the physical spin density and the effect of slice select gradients.

A look at ... - shape and function from motion in biomedical imaging: part 3

The understanding of multilevel mechanisms, with all the complexity they have from a basic and disease-oriented perspective, is a major challenge and will mobilize a large part of biomedical research in the future. The study of protein and cell assemblies, in such conditions that their intricated cascades of behaviors will be preserved, can not only bring answers to biological and medical questions but also lead to unsuspected issues. Image analysis, applied to joint observations at nanometer to millimeter scales, with its potential to extract and quantify structures and shapes and to track proteins and cells, should bring new insights on mechanisms of life. Significant breakthroughs at experimental and methodological levels are still required to achieve this goal. From an image-processing viewpoint, there is a demand for fully innovative algorithms that will be independent of image intensity variations, shape and topology changes, and any priors on movements. Cutting-edge technologies for in-vivo explorations (e.g., the only natural environment), beyond the endo-microscopes already available, will merge with the image-guided world developed in computer-assisted medicine.

Measures of structural complexity in digital images for monitoring the ecological signature of an old-growth forest ecosystem

Conducting field samples for monitoring ecological dynamics across multiple spatiotemporal scales is a difficult task using standard protocols. One alternative is to measure a restricted set of variables which can serve as an ecological orientor (EO) for quantifying habitat change. The objective of this article is to derive from digital images a measure of structural complexity that may serve as a proximate EO for monitoring forest dynamics in space and time. The mean information gain (MIG) index was used as a measure of structural complexity in photographs taken directly in the field over the entire growing season. At a small scene extent, the complexity of light intensity variations in digital images was positively related to species richness. At larger scene extents, forest understorey and overstorey layers showed predictable ecological signatures in structural complexity. In general, intensity and chroma were the two color space components which yielded the greatest sensitivity to habitat change through time. Within the framework of a standardized photographic protocol, it seems therefore reasonable to consider MIG as a suitable EO for monitoring forest dynamics in both space and time. Our results support the idea that it is possible on one hand to adopt a more holistic view of ecological processes to gain, on the other hand, spatial and temporal degrees of freedom for testing multiple scale hypotheses in the field.