An improved pixel-based approach for analyzing images in two-dimensional gel electrophoresis

An improved pixel-based approach for analyzing 2-DE images is presented. The key feature of the method is to create a mask based on all gels in the experiment using image morphology, followed by multivariate analysis on the pixel level. The method reduces the impact of noise and background by identifying regions in the image where protein spots are present, but make no assumption on individual spot boundaries for isolated spots. This makes it possible to detect significant changes in complex regions, and visualize these changes over multiple gels in an easy way. False missing values and spot volumes caused by imposing erroneous spot boundaries are thus circumvented. The approach presented gives improved pixel-based information from the gels, and is also an alternative to existing methods for data-reduction, significance testing and visualization of 2-DE data. Results are compared with software using a common spot boundary approach on an experiment consisting of 35 full size gel images. Gel alignment is required before analysis.     

Robust automated three-dimensional segmentation of secondary ion mass spectrometry image sets

Three-dimensional (3-D) element distributions generated by scanning secondary ion mass spectrometry (SIMS) are usually noisy and blurred and contain objects which do not usually have sharp edges or may have noise induced boundaries. Additionally, there are local intensity differences due to sensitivity differences of the channelplate. As a result, traditional segmentation techniques become difficult and yield rather poor results. We present a novel methodology which combines a restoration process (using a combination of channelplate sensitivity compensation with a 3-D de-noising technique based on the wavelet transform) with a fuzzy logic 3-D gray level segmentation which can be used to successfully segment 3-D SIMS image sets. The restoration algorithm removes the artifacts produced by the channelplate inhomogeneities as well as noise aberrations from the image sets and the gray level thresholding algorithm segments their features. The algorithm is designed for minimal user interaction to achieve a high automation level. The methodology is discussed and experimental results using real 3-D images are presented.     

Enhanced feature analysis using wavelets for scanning probe microscopy images of surfaces

In this work we develop wavelet theory for the analysis of surface topography images obtained by scanning probe microscopy (SPM) such as atomic force microscopy (AFM). Wavelet transformation is localized in space and frequency, which can offer an advantage for analyzing information on surface morphology and topography. Wavelet transformation is an ideal tool to detect trends, discontinuities, and short periodicities on a surface. Additionally, wavelets can be used to remove artifacts and noise from scanning microscopy images. In terms of 3-D image analysis, discrete wavelet transform can capture patterns at all relevant frequency scales, thus providing a level of image analysis that is not possible otherwise. It is also possible to use the methodology for analyzing surface structures at the molecular level. The results demonstrate superior capabilities of wavelet approach to scanning probe microscopy image analysis compared to traditional analysis techniques.     

A simulation study to evaluate the statistical noise and spatial resolution in image reconstruction of emission computed tomography--with respect to the optimization of the filter function in the convolution integral

Filtered backprojection method has been commonly used to reconstruct images in the field of the computed tomography (CT). However, in the emission CT such as positron and single photon CT, poor counting static which are caused by limited dosage to patients, limited counting rate capacity and limited efficiency of the imaging device, produce a statistical noise in the reconstructed image. The magnitude of the statistical noise and the spatial resolution were evaluated for various shapes of the filter used in the convolution integrals of the filtered back-projection procedure. The statistical noise was proportional to the inverse of the root of the total number of counts for any filters. The high-frequency-cut characteristic of the filter reduced the statistical noise, but increased the spatial resolution in the images. It was possible to optimize the shape of the filter for given total number of counts and required statistical noise and spatial resolution.     

Off-Line Metrology on SEM Images Using Gray Scale Morphology

Discrimination and metrology results of microlithographic patterns from top-down SEM images are explored by means of morphological image analysis. The method relies on the use of various morphological filters on a top down SEM image. The resulted images are segmented in order to derive a quality factor which discriminates the candidate images as under- or fully-developed. Furthermore, the fully developed images are processed in order to extract useful measurements. The proposed image analysis methodology achieves for first time, to the authors’ knowledge, successful off-line discrimination between under-developed and fully-developed cases. For the latter case, the measuring method relies upon the evaluation of the connected regions in the SEM image after segmentation. This is expressed by the Useful Threshold Range (UTR), which corresponds to that specific value of connected regions obtained for the wider range of the threshold. The method is experimentally demonstrated by employing 72 test images from high resolution patterns. The evaluated critical pattern parameters are found in good agreement to those derived from on-line procedures.     

Structure and chemistry of interfaces

It is most desirable to understand the structure and chemistry of the internal interfaces for all classes of materials since the materials' properties often depend on the properties of the interfaces which, in turn, are controlled by their structure and chemistry. In contrast to surface science, there exist only a few techniques for studying the structure and chemistry of internal interfaces. One of the most powerful techniques seems to be transmission electron microscopy (TEM) by which short segments of interfaces can be analyzed. In high-resolution electron microscopy (HREM) a direct image is formed of the projection of the interfaces. A simple analysis of HREM micrographs is not possible owing to the complex image forming processes within HREM. In addition to experimental investigations, calculations of the structures must be performed using material specific interatomic potentials. From the calculated structure, HREM images must be simulated for the specific imaging conditions. The experimental micrographs must be compared to simulated images. An agreement between experimental micrographs and the simulated images results in the best possible atomistic configuration. A quantitative measure for this agreement is the difference image, D, between the experimental micrograph and the simulated image. Best agreement is reached if only the noise is visible in the difference image D. Analytical electron microscopy with high-spatial resolution (typical probe size <0.05 nm) allows the identification of impurities segregated at the interface. However the limit of detectability depends sensitively on the combination between the different elements. Recently developed techniques on spatially resolved electron energy loss spectra give information on bonding and coordination. As an example, the different TEM techniques have been applied to the investigation of grain boundaries in -Al₂O₃. It should be emphasized, however, that the TEM techniques could also be applied to internal interfaces in different boundaries.     

NanoSIMS image enhancement by reducing random noise using low-rank method

NanoSIMS images are usually affected by random noises because of various types of sources, which degrade the quality of ion images and increase the uncertainty of the geochemical interpretations. Here, we applied the weighted nuclear norm minimization (WNNM) method to reduce the random noise in the NanoSIMS image. The low-rank property of the image is fully considered to suppress random noise while retaining reliable details of weak signals. Numerical experiments on four different kinds of NanoSIMS ion images show that the denoising ability of the WNNM method is superior to that of the median filter, no matter the size of the filtering windows used (eg, 3 × 3, 5 × 5, and 7 × 7). The WNNM method can reduce random noise while preserving the most critical details in the original NanoSIMS observations, which can significantly enhance reliability when distinguishing critical boundaries and structures.     

Automatic matching of SAM, SIMS and EPMA images

Matching and analysis of dissimilar images of a given scene gathered from different sources are important processes for their integration (e. g. in the course of a multisource interpretation or quantification). However, prior to the analysis, geometric distortions caused by the acquisition process have to be eliminated. This problem involves two steps: the extraction of dominant image features (control structures), such as edges or regions with homogeneous elemental coverage and the estimation of the model parameters of the geometric transformation based on the calculated features. An approach for a 5-parameter automatic matching process for data from SAM (Scanning Auger Microscopy), SIMS (Secondary Ion Mass Spectroscopy) or EPMA (Eletron Probe Micron Analysis) multispectral images is presented. The parameters include global translation, rotation and scaling.     

Simple statistically based alternatives to MAF for ToF‐SIMS spectral image analysis

The maximum autocorrelation factors technique (MAF) is becoming increasingly popular for the multivariate analysis of spectral images acquired with time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) instruments. In this article, we review the conditions under which the underlying chemical information can be separated from the large‐scale, non‐uniform noise characteristic of ToF‐SIMS data. Central to this pursuit is the ability to assess the covariance structure of the noise. Given a set of replicate images, the noise covariance matrix can be estimated in a straightforward way using standard statistical tools. Acquiring replicate images, however, is not always possible, and MAF solves a subtly different problem, namely, how to approximate the noise covariance matrix from a single image when replicates are not available. This distinction is important; the MAF approximation is not an unbiased statistical estimate of the noise covariance matrix, and it differs in a highly significant way from a true estimate for ToF‐SIMS data. Here, we draw attention to the fact that replicate measurements are made during the normal course of acquiring a ToF‐SIMS spectral image, rendering the MAF procedure unnecessary. Furthermore, in the common case that detector dead‐time effects permit no more than one ion of any specific species to be detected on a single primary ion shot, the noise covariance matrix can be estimated in a particularly simple way, which will be reported. Copyright © 2011 John Wiley & Sons, Ltd.