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Multiframe super-resolution (SR) reconstruction of small moving objects against a cluttered background is difficult for two reasons: a small object consists completely of “mixed” boundary pixels and the background contribution changes from frame-to-frame. We present a solution to this problem that greatly improves recognition of small moving objects under the assumption of a simple linear motion model in the real-world. The presented method not only explicitly models the image acquisition system, but also the space-time variant fore- and background contributions to the “mixed” pixels. The latter is due to a changing local background as a result of the apparent motion. The method simultaneously estimates a subpixel precise polygon boundary as well as a high-resolution (HR) intensity description of a small moving object subject to a modified total variation constraint. Experiments on simulated and real-world data show excellent performance of the proposed multiframe SR reconstruction method.

Today, the resolution in phase-contrast cryo-electron tomography is for a significant part limited by the contrast transfer function (CTF) of the microscope. The CTF is a function of defocus and thus varies spatially as a result of the tilting of the specimen and the finite specimen thickness. Models that include spatial dependencies have not been adopted in daily practice because of their high computational complexity. Here we present an algorithm which reduces the processing time for computing the ‘tilted’ CTF by more than a factor 100. Our implementation of the full 3D CTF has a processing time on the order of a Radon transform of a full tilt-series. We derive and validate an expression for the damping envelope function describing the loss of resolution due to specimen thickness. Using simulations we quantify the effects of specimen thickness on the accuracy of various forward models. We study the influence of spatially varying CTF correction and subsequent tomographic reconstruction by simulation and present a new approach for space-variant phase-flipping. We show that our CTF correction strategies are successful in increasing the resolution after tomographic reconstruction.

There is an ongoing debate on how to model diffusivity in fiber crossings. We propose an optimization framework for the selection of a dual tensor model and the set of diffusion weighting parameters, such that both the diffusion shape and orientation parameters can be precisely as well as accurately estimated. For that, we have adopted the Cramer-Rao lower bound (CRLB) on the variance of the model parameters, and performed Monte Carlo simulations. We have found that the axial diffusion lambda(vertical bar vertical bar) needs to be constrained, while an isotropic fraction can be modeled by a single parameter f(iso). Under these circumstances, the Fractional Anisotropy (FA) of both tensors can theoretically be independently estimated with a precision of 9% (at SNR = 25). Levenberg-Marquardt optimization of the Maximum Likelihood function with a Rician noise model approached this precision while the bias was insignificant. A two-element b-vector b = [10.0 3.5]. 10(3) mm(-2)s was found to be sufficient for estimating parameters of heterogeneous tissue with low error. This has allowed us to estimate consistent FA-profiles along crossing tracts. This work defines fundamental limits for comparative studies to correctly analyze crossing white matter structures

Diagnosing of injuries of the wrist bones is problematic due to a highly complex and variable geometry. knowledge of variations of healthy bone shapes is essential to detect wrist pathologies, developing prosthetics and investigating biomechanical properties of the wrist joint. In previous literature various methods have been proposed to classify different scaphoid and lunate types. These classifications were mainly qualitative or were based on a limited number of manually determined surface points. The purposes of this study are to develop a quantitative, standardized description of the variations in the scaphoid and lunate and to investigate whether it is feasible to divide carpal bones in isolated shape categories based on statistical grounds. The shape variations of the scaphoid and lunate were described by constructing a statistical shape model (SSM) of healthy bones. SSM shape parameters were determined that describe the deviation of each shape from the mean shape. The first five modes of variation in the SSMs describe 60% of the total variance of the scaphoid and 57% of the lunate. Higher modes describe less than 5% of the variance per mode. The distributions of the parameters that characterize the bone shape variations along the modes do not significantly differ from a normal distribution. The SSM provides a description of possible shape variations and the distribution of scaphoid and lunate shapes in our population at an accuracy of approximately the voxel size (0.3 x 0.3 x 0.3 mm(3)). The developed statistical shape model represents the previously qualitatively described variations of scaphoid and lunate. However, strict classifications based on shape differences are not feasible on statistical grounds. (C) 2010 Elsevier Ltd. All rights reserved

The design of most ulnar head implants is currently not based on studies of anatomical variations. To enable prosthesis design based on anatomical features, a method is developed for the detection of the ulna surface that articulates with the radius head. The articulating surface is detected by combining partial articulating ulna surfaces detected in CT scans of different individuals in different poses. Correspondences between ulnae of different individuals are established through the construction of a statistical shape model (SSM) of the ulna head. The articulating surface is attached to this SSM, allowing the detection of articulating surfaces in ulnae that are not in the training set of the model. As a simple shape is desirable for prosthesis design, three quadratic surfaces were fitted to the articulating surfaces of 40 ulnae. The mean fitting error for the simplest surface, a cylinder, was 0.20 mm. As this error is smaller than the voxel size of 0.3 mm isotropic, it was concluded that the articulating ulna surface can be satisfactory approximated by a cylinder part.

When bright moving objects are viewed with an electro-optical system at very long range, they will appear as small slightly blurred moving points in the recorded image sequence. Detection of point targets is seriously hampered by structure in the background, temporal noise and aliasing artifacts due to undersampling by the infrared (IR) sensor. Usually, the first step of point target detection is to suppress the clutter of the stationary background in the image. This clutter suppression step should remove the information of the static background while preserving the target signal energy. Recently we proposed to use super-resolution reconstruction (SR) in the background suppression step. This has three advantages: a better prediction of the aliasing contribution allows a better clutter reduction, the resulting temporal noise is lower and the point target energy is better preserved. In this paper the performance of the point target detection based on super-resolution reconstruction (SR) is evaluated. We compare the use of robust versus non robust SR reconstruction and evaluate the effect of regularization. Both of these effects are influenced by the number of frames used for the SR reconstruction and the apparent motion of the point target. We found that SR improves the detection efficiency, that robust SR outperforms non-robust SR, and that regularization decreases the detection performance. Therefore, for point target detection one can best use a robust SR algorithm with little or no regularization.

We propose a novel technique for tract-based comparison of DTI-indices between groups, based on a representation that is estimated while matching fiber tracts. The method involves a non-rigid registration based on a joint clustering and matching approach, after which a 3D-atlas of cluster center points is used as a frame of reference for statistics. Patient and control FA-distributions are compared per cluster. Spatial consistency is taken to reflect a significant difference between groups. Accordingly, a non-parametric classification is performed to assess the continuity of pathology over larger tract regions. In a study to infant survivors treated for medulloblastoma with intravenous methotrexate and cranial radiotherapy, significant decreases in FA in major parts of the corpus callosum were found.

The purpose of this research was to evaluate the relation between preferential direction of pores and mechanical strength of cubic starch compacts. The preferential pore direction was quantified in SEM images of cross sections of starch compacts using a previously described algorithm for determination of the quotient of transitions (Q). This parameter and the mechanical strength were evaluated in compacts of different porosities. Starch was chosen as a model compound for materials with ductile behaviour of which tablets with low porosities can be made and which shows some elastic recovery after compaction. At medium and high porosity Q was significantly higher in the images providing a side view of the compact than in the images providing a top view (0.973 vs. 0.927 and 0.958 vs. 0.874 at 0 mm from the side of the compact and 0.956 vs. 0.854 and 0.951 vs. 0.862 at 3.5 mm), indicating that the pores were mainly oriented in the direction perpendicular to the direction of compression. This was accompanied by a lower crushing force in this direction. This could be explained by considering the pores as cracks which propagate through the sample during crushing. For both directions the crushing force decreased with increasing porosity. The yield strength of the compacts also decreased with increasing porosity, but this parameter was not dependent on the direction of crushing when the porosity was below 10%. The results show that pore direction significantly influences the crushing force but does not influence the yield strength, at porosities below 10%. © American Association of Pharmaceutical Scientists 2008.

The present research aims to test the hypothesis that the addition of a minor component causes a change in pore shape in the matrix of the primary component, causing a decrease in mechanical strength. Tablets made of sodium chloride only and tablets made of a mixture of sodium chloride (97.5% v/v) and starch (2.5% v/v) were compared. Tablets were subjected to a heat treatment to remove the starch. The pore structure was evaluated with mercury porosimetry and image analysis on SEM images. At comparable porosities the tensile strength of the mixture tablets was significantly lower than that of the tablets made of NaCl only, Visual inspection of the images suggested a structure with less connectivity of the grains for the heat treated mixture tablets. This was confirmed by the results of the algorithm calculating the relative path length. Image analysis showed that the pore size distribution shifted towards larger pores after the addition of starch. It was thus concluded that the lower mechanical strength of the tablets made of the binary mixture was caused by the more open pore structure and more larger pores as could be detected with image analysis. (C) 2008 Elsevier B.V. All rights reserved.

In order to retrieve a high-resolution reservoir model from seismic and well data, an approach was developed based on an a priori layered model from well data, specifically the acoustic impedances derived from the sonic and density logs. The procedure consists of using a forward model of the well data as a priori information that is then iteratively matched with the seismic data using a Bayesian inversion process. The inversion is then extended to 2D, whereby the extrapolation is guided by a simple geometric envelope described with a small number of parameters. It is tested on a seismic data set containing a deltaic clinoform in the North Sea, whereby the clinoform geometry is parameterized by a sigmoid and used as prior information. In the subsequent optimization the clinoform geometry is further refined with a limited number of local knots to improve the match with the seismic data. This lowparameterization inversion approach thus uses geological shapes and well constraints to obtain a subsurface model than can have a substantially higher resolution than the seismic wavelength.

In Diffusion Tensor Imaging (DTI), differently oriented fiber bundles inside one voxel are incorrectly modeled by a single tensor. High Angular Resolution Diffusion Imaging (HARDI) aims at using more complex models, such as a two-tensor model, for estimating two fiber bundles. We propose a new method for creating experimental phantom data of fiber crossings, by mixing the DWI-signals from high FA-regions with different orientation. The properties of these experimental phantoms approach the conditions of real data. These phantoms can thus serve as a 'ground truth' in validating crossing reconstruction algorithms. The angular resolution of a dual tensor model is determined using series of crossings, generated under different angles. An angular resolution of 0.67pi was found in data scanned with a diffusion weighting parameter b = 1000 s/mm2. This resolution did not change significantly in experiments with b = 3000 and 5000 s/mm2, keeping the scanning time constant.

Over the past years many computer aided diagnosis (CAD) schemes have been presented for the detection of colonic polyps in CT Colonography. The vast majority of these methods (implicitly) model polyps as approximately spherical protrusions. Polyp shape and size varies greatly, however and is often far from spherical. We propose a shape and size invariant method to detect suspicious regions. The method works by locally deforming the colon surface until the second principal curvature is smaller than or equal to zero. The amount of deformation is a quantitative measure of the 'protrudeness'. The deformation field allows for the computation of various additional features to be used in supervised pattern recognition. It is shown how only a few features are needed to achieve 95% sensitivity at 10 false positives (FP) per dataset for polyps larger than 6 mm.

Summary Many important details of potential subsurface reservoirs that we wish to characterize are only indirectly present in the reflected wavefields measured at the Earth’s surface. Therefore, the analysis of seismic data always presents an inversion problem. Instead of analyzing the data trace by trace, we propose an automated procedure that adjusts the parameters of a two-dimensional geological model by minimizing the mismatch between the simulated and measured seismic. This approach differs from standard inversion problems in that the size of the required details of the 2D geological reservoir model is far below the limits of the seismic resolution.

Porosity and pore structure are important characteristics of tablets, since they influence mechanical strength and many other proper-ties. This paper proposes an alternative method for the characterization of pore structure based on image analysis of SEM micrographs. SEM images were made of sodium chloride tablets made with three different particle sizes. The pore size distribution in these images was determined with a technique referred to as a morphological sieve. The results were compared to the pore size distributions as obtained with mercury porosimetry. The SEM images display small cracks inside the grains and small 'floating' grains inside the pore space. As these artifacts are induced in sample preparation, they need to be identified and removed from the images before analysis. The influence of the size of the discarded structures on the total porosity and the pore size distribution was investigated. The small 'floating' grains prevented the determination of the size of large pores, but had a negligible effect on the porosity. The removal of small cracks inside the grains had no effect on the pore size distribution but a large effect on the porosity. Based on the comparison of these results with the experimentally determined porosity, a maximum size for the structures that were to be removed was determined. The resulting pore size distributions were in the same order of magnitude as the results obtained with mercury porosimetry. Both methods display a comparable relative shift of the pore size distributions to larger sizes for tablets with increasing particle size. Therefore, it can be concluded this image analysis technique is a good method for the characterization of pore structure. (c) 2007 Elsevier B.V. All rights reserved.

Tortuosity is defined as the ratio of the actual path length from start to goal through the pores to the Euclidean distance (shortest linear distance). However, traditional methods to calculate tortuosity, such as the calculation of tortuosity out of the results from dissolution experiments, do not determine path length directly. In this paper we describe the application of image analysis for direct measurement of path length in order to obtain quantitative information on tortuosity. Several planes in cubic sodium chloride compacts, made by uni-axial compression, were imaged using scanning electron microscopy (SEM). In these images the average path length from top-to-bottom and from left-to-right was calculated, using the gray-weighted distance transform (GDT). As the direct, straight forward path was defined as having a length of unity, the relative path length could be taken as a quantitative measure for the tortuosity. The relative path length through the pores was found to be 1.4-1.6 and the relative path length over the grains was significantly lower (1.0-1.2). In most cases, the relative path length through the pores was significantly higher for the compacts containing small particles than for the compacts made of large particles. The relative path length was also dependent on the direction of the measurement, i.e. in the direction of compression of the compact or perpendicular to it. This indicates anisotropy in structure with the pores preferentially oriented in the direction of compression. It was concluded that this method is a valuable tool for the determination of path length in compacts as a direct measure for tortuosity. It can also be used to evaluate the anisotropy in structure. © 2006 Elsevier B.V. All rights reserved.

This paper proposes an example-based Super-Resolution (SR) algorithm of compressed videos in the Discrete Cosine Transform (DCT) domain. Input to the system is a Low-Resolution (LR) compressed video together with a High-Resolution (HR) still image of similar content. Using a training set of corresponding LR-HR pairs of image patches from the HR still image, high-frequency details are transferred from the HR source to the LR video. The DCT-domain algorithm is much faster than example-based SR in spatial domain 6 because of a reduction in search dimensionality, which is a direct result of the compact and uncorrelated DCT representation. Fast searching techniques like tree-structure vector quantization 16 and coherence search 1 are also key to the improved efficiency. Preliminary results on MJPEG sequence show promising result of the DCT-domain SR synthesis approach.

Over the past years many computer aided diagnosis (CAD) schemes have been presented for the detection of colonic polyps in CT Colonography. The vast majority of these methods (implicitly) model polyps as approximately spherical protrusions. Polyp shape and size varies greatly, however and is often far from spherical. We propose a shape and size invariant method to detect suspicious regions. The method works by locally deforming the colon surface until the second principal curvature is smaller than or equal to zero. The amount of deformation is a quantitative measure of the 'protrudeness'. The deformation field allows for the computation of various additional features to be used in supervised pattern recognition. It is shown how only a few features are needed to achieve 95% sensitivity at 10 false positives (FP) per dataset for polyps larger than 6 mm

Conventional enzymatic assays for alcohol dehydrogenase, pyruvate kinase, and enolase performed in 96-well microtiter plates were compared with assays monitored in 25-well nanoarrays. All miniaturized reactions could be performed in maximum volumes of 6.3-8 nL and were read out with a conventional fluorescence microscope system equipped with a scientific grade CCD camera. Substrate and cofactor were already present inside the wells after having been presprayed, or they were applied in solution to the wells of the nanoarray shortly before the assays started. For all of the assays, commercially available enzymes and enzymes present in cell-free extracts were used. Assays carried out in premixed nanoarrays gave results comparable to those performed in presprayed nanoarrays. Enzyme activities determined in nanoarrays by using two different methods were in good agreement with assays performed in microtiter plates. Also, good correspondence was found between expected and observed enzyme levels. In short, enzymatic assays performed in premixed and in particular in presprayed nanoarrays are a promising low-volume and low-reagent- and sample-consuming alternative to current methodology and could find applications in many different areas of analytical chemistry.

Recently, an extraordinary transmission of light through small holes (

In previous publications and presentations we have described our construction of a laboratory-on-a-chip based on nanoliter capacity wells etched in silicon. We have described methods for dispensing reagents as well as samples, for preventing evaporation, for embedding electronics in each well to measure fluid volume per well in real-time, and for monitoring the production or consumption of NADH in enzyme-catalyzed reactions such as those found in the glycolytic pathway of yeast. In this paper we describe the use of light sensors (photodiodes) in each well to measure both fluorescence (such as that evidenced in NADH) as well as bioluminescence (such as evidenced in ATP assays). We show that our detection limit for NADH fluorescence in 100 μM and for ATP/luciferase bioluminescence is 2.4 μM.

Publisher Summary This chapter discusses the application of neural networks in image processing. Many researchers have attempted to apply artificial neural networks (ANNs) to image processing problems. It is an overview of what can now perhaps be called the "neural network hype" in image processing. In some of these applications the most interesting aspect of ANNs, the fact that they can be trained, was not (or only partly) used. This held especially for applications to the first few tasks in the image processing chain: preprocessing and feature extraction. Another advantage of ANNs often used to justify their use is the ease of hardware implementation; however, in most publications, this did not seem to be the reason for application. The experiment on supervised classification, in handwritten digit recognition, showed that ANNs are quite capable of solving difficult object recognition problems. A number of ANN architectures were trained to mimic the Kuwahara filter, a nonlinear edge-preserving smoothing filter used in preprocessing. The experiments showed that careful construction of the training set is very important. ANNs seem to be most applicable for problems requiring a nonlinear solution, for which there is a clear, unequivocal performance criterion.

In this paper we describe a new strategy for using local structure adaptive filtering in normalized convolution. The shape of the filter, used as the applicability function in the context of normalized convolution, adapts to the local image structure and avoids filtering across borders. The size of the filter is also adaptable to the local sample density to avoid unnecessary smoothing over high certainty regions. We compared our adaptive interpolation technique with conventional normalized averaging methods. We found that our strategy yields a result that is much closer to the original signal both visually and in terms of MSE, meanwhile retaining sharpness and improving the SNR.

Surfaces can be described locally and classified by their curvature values at every point. In this paper we investigate a grey-level based curvature estimator in combination with a sampling-error free integration technique of the curvature image. We compute shape descriptors as the bending energy and a global topological invariant, the Euler characterization. The integration of curvature values over the surface area is done by grey-volume integration. Our curvature estimator works on the orientation field of the surface, which does not require a segmentation of the surface. The estimated orientation fields has discontinuities mod π. It is mapped via the Knutsson mapping to a continuous representation in which the curvatures are computed.

Whole Slide Imaging (WSI) systems are high-throughput automated microscopes for digital pathology applications. We present a method for testing and monitoring the optical quality of WSI-systems using a measurement of the through-focus Optical Transfer Function (OTF) obtained from the edge response of a custom made resolution target, composed of sagittal and tangential edges. This enables quantitative analysis of a number of primary aberrations. The curvature of the best focus as a function of spatial frequency is indicative for spherical aberration, the argument of the OTF quantifies for coma, and the best focus as a function of field position for sagittal and tangential edges allows assessment of astigmatism and field curvature. The statistical error in the determined aberrations is typically below 20 mλ. We use the method to compare different tube lens designs and to study the effect of objective lens aging. The results are in good agreement with direct measurement of aberrations based on Shack-Hartmann wavefront sensing with a typical error ranging from 10 mλ to 40 mλ.

Yeast - Saccharomyces cerevisiae- is widely used as a model system for other higher eukaryotes, including man. One of the basic fermentation processes in yeast is the glycolytic pathway, which is the conversion of glucose to ethanol and carbon dioxide. This pathway consists of 12 enzyme-catalyzed reactions. With the approach of microarray technology we want to explore the metabolic regulation of this pathway in yeast. This paper will focus on the design of a conventional microscope based microarray reader, which is used to monitor these enzymatic reactions in microarrays. These microarrays are fabricated in silicon and have sizes of 300◊300µm 2 . The depth varies from 20 to 50µm. Enzyme activity levels can be derived by monitoring the production or consumption rate of NAD(P)H, which is excited at 360nm and emits around 450nm. This fluorophore is involved in all 12 reactions of the pathway. The microarray reader is equipped with a back-illuminated CCD camera in order to obtain a high quantum efficiency for the lower wavelengths. The dynamic range of our microarray reader varies from 5µMolar to 1mMolar NAD(P)H. With this microarray reader enzyme activity levels down to 0.01 unit per milliliter can be monitored. The acquisition time per well is 0.1s. The total scan cycle time for a 5 ◊ 5 microarray is less than half a minute. The number of cycles for a proper estimation of the enzyme activity is inversely proportional to the enzyme activity: long measurement times are needed to determine low enzyme activity levels.

We are developing a method for high-throughput screening using arrays of `nanowells' built into a silicon substrate. These wells can serve as bioreactors for studying a variety of biochemical reactions such as the enzymatic activity that occurs in yeast metabolism. For a variety of studies it is important to know the volume of liquid that has been deposited in a given well and/or to monitor the evaporation of the liquid. Using silicon as our substrate means that we can take advantage of the ability to build microelectronics into the wells in order to develop `smart' wells.

We present various methods to increase the spatial resolution of an undersampled, and thus aliased, image sequence. The sequence is acquired by an infrared camera, which severely undersamples the image of a static scene. Vibration of the setup causes a random, sub-pixel, global translation of the scene before sampling. Although a single frame is hampered by aliasing, a sufficiently large series of such frames contains all the information needed to undo this aliasing. The global translation among the frames is estimated based only on the image content. These shift estimates enable us to fuse the frames in the sequence, providing a high-resolution image, which is done using interpolation schemes applicable to non-uniformly sampled data. These interpolation schemes have a built-in regularization to balance resolution enhancement and increase of signal-to-noise ratio. The proposed methods are of moderate computational complexity and can be implemented in hardware for real-time use. They prove to be robust under noisy circumstances, and the performance degrades “gracefully” when the number of input frames decreases. We compare our results with alternative methods involving iterative algorithms, which are not practical for real-time application, and a spectral estimation algorithm, which is too sensitive to noise and errors in the shift estimation.

The goal of our TU Delft interfaculty research program is to develop intelligent molecular diagnostic systems (IMDS) that can analyze liquid samples that contain a variety of biochemical compounds such as those associated with fermentation processes. One specific project within the IMDS program focuses on photon sensors. In order to analyze the liquid samples we use dedicated microarrays. At this stage, these are basically miniaturized micro titre plates. Typical dimensions of a vial are 200 X 200 X 20 micrometer3. These dimensions may be varied and the shape of the vials can be modified with a result that the volume of the vials varies from 0.5 to 1.6 nl. For all experiments, we have used vials with the shape of a truncated pyramid. These vials are fabricated in silicon by a wet etching process. For testing purposes the vials are filled with rhodamine solutions of various concentrations. To avoid evaporation glycerol-water (1:1, v/v) with a viscosity of 8.3 times the viscosity of water is used as solvent. We aim at wide field-of-view imaging at the expense of absolute sensitivity: the field-of-view increases quadratically with decreasing magnification. Small magnification, however, implies low Numerical Aperture (NA). The ability of a microscope objective to collect photons is proportional to the square of the NA. To image the entire microarray we have used an epi-illumination fluorescence microscope equipped with a low magnification (2.5 X/0.075) objective and a scientific CCD camera to integrate the photons emitted from the fluorescing particles in the solutions in the vials. From these experiments we found that for this setup the detection limit is on the order of micromolar concentrations of fluorescing particles. This translates to 108 molecules per vial.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The essential difference of non-linear image restoration algorithms with linear image restoration filters is their capability to restrict the restoration result to non-negative intensities. The iterative constrained Tikhonov-Miller algorithm (ICTM) algorithm, for example, incorporates the non-negativity constraint by clipping each iteration of its conjugate gradient descent algorithm. This constraint will only be effective when the restored intensities have near zero values. Therefore the background estimation will have an influence on the effectiveness of the non-negativity constraint of these non-linear restoration algorithms. We have investigated the effect of the background estimation on the performance of the ICTM, Carrington, and RichardsonLucy algorithms and compared it to the performance of the linear Tikhonov-Miller restoration filter. We found that an underestimation of the background will make the non-negativity constraint ineffective which results in a performance that does not differ much from the performance obtained by the linear restoration filter. An overestimation of the background however is even more dramatic since it results in a clipping of object intensities. We show that this will dramatically deteriorate the performance of the non-linear restoration algorithms. We propose a novel method to estimate the background based on the dependency of non-linear restoration algorithms on the background.

Recognition Group, Faculty ofApplied Physics, Delft University of TechnologyLorentzweg 1, 2628 CJ Delft, The Netherlands.ABSTRACTInterference- and phase-contrast microscopy are techniques developed to visualize transparent objectswithout the incorporation of stains, allowing observations on living cells. The development of CCD cameras andimage analysis technology in the last decades permits quantitative analysis of interferometric data, obtained byinterference microscopy. A method is presented to determine the optical pathlength at each position in digitizedmicroscopic images.Unstained cells and artificial testobjects were observed under a standard, commercially available, NomarskiDifferential Interference Contrast (DIC) microscope, using monochromatic light. A cooled CCD camera was used torecord images. Image processing software was used to convert the measured intensities to phase differences. Awiener filter based on the typical lateral shift introduced by the DIC microscope, was applied to reconstruct theoriginal optical pathlength distribution.This system, based upon a conventional DIC microscope, rather than upon a purpose build interferometric scanningsystem, allows easier determination of optical pathlength differences. After image acquisition and determination ofoptical pathlength distribution, analysis of cell parameters can be performed using standard image processingsoftware. Dry mass- or DNA content, for instance, can be estimated by integrating the optical pathlength over thecellular surface.Keywords: DIC, Nomarsk4 Interference, Microscopy, CCD, Image Processing, Analysis, Reconstruction, OpticalPathlength, Phase, Transparent, Living.1. INTRODUCTIONInterference- and phase-contrast microscopy are techniques invented more than 60 years ago to visualize

The analysis of the three-dimensional structure of tissue, cells and cellular constituents play a major role in biomedical research. Three-dimensional images, acquired by confocal fluorescence microscopes play a key role in this analysis. However, the imaging properties of these microscopes give rise to diffraction-induced blurring phenomena. These distortions hamper subsequent quantitative analysis. Therefore, restoration algorithms that invert these distortions will improve these analyses. We have tested the performances of the Richardson-Lucy, and the ICTM algorithm in a simulation experiment and found a strong dependency of their performances on the signal-to-noise ratio of the image. We propose a pre-filtering to reduce the noise in the image without hampering the object. We have applied a Gaussian filter and a median filter prior to the restoration, and compensate for this extra blurring of the Gaussian in the restoration procedure. We show how this pre- filtering improves the performance of the restoration algorithms. The experiments were performed on spheres convolved with a confocal point spread function and distorted with Poisson noise.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Purpose: There is increasing evidence that epicardial fat (i.e., adipose tissue contained within the pericardium) plays an important role in the development of cardiovascular disease. Obtaining the epicardial fat volume from routinely performed non-enhanced cardiac CT scans is therefore of clinical interest. The purpose of this work is to investigate the feasibility of automatic pericardium segmentation and subsequent quantification of epicardial fat on non-enhanced cardiac CT scans. Methods: Imaging data of 98 randomly selected subjects belonging to a larger cohort of subjects who underwent a cardiac CT scan at our medical center were retrieved. The data were acquired on two different scanners. Automatic multi-atlas based method for segmenting the pericardium and calculating the epicardial fat volume has been developed. The performance of the method was assessed by (1) comparing the automatically segmented pericardium to a manually annotated reference standard, (2) comparing the automatically obtained epicardial fat volumes to those obtained manually, and (3) comparing the accuracy of the automatic results to the inter-observer variability. Results: Automatic segmentation of the pericardium was achieved with a Dice similarity index of 89.1 ± 2.6% with respect to Observer 1 and 89.2 ± 1.9% with respect to Observer 2. The correlation between the automatic method and the manual observers with respect to the epicardial fat volume computed as the Pearson's correlation coefficient (R) was 0.91 (P < 0.001) for both observers. The inter-observer study resulted in a Dice similarity index of 89.0 ± 2.4% for segmenting the pericardium and a Pearson's correlation coefficient of 0.92 (P < 0.001) for computation of the epicardial fat volume. Conclusions: The authors developed a fully automatic method that is capable of segmenting the pericardium and quantifying epicardial fat on non-enhanced cardiac CT scans. The authors demonstrated the feasibility of using this method to replace manual annotations by showing that the automatic method performs as good as manual annotation on a large dataset.

Purpose: The degree of stenosis is an important biomarker in assessing the severity of cardiovascular disease. The purpose of our work is to develop and evaluate a semiautomatic method for carotid lumen segmentation and subsequent carotid artery stenosis quantification in CTA images. Methods: The authors present a semiautomatic stenosis detection and quantification method following lumen segmentation. The lumen of the carotid arteries is segmented in three steps. First, centerlines of the internal and external carotid arteries are extracted with an iterative minimum cost path approach in which the costs are based on a measure of medialness and intensity similarity to lumen. Second, the lumen boundary is delineated using a level set procedure which is steered by gradient information, regional intensity information, and spatial information. Special effort is made in adding terms based on local centerline intensity prior so as to exclude all possible plaque tissues from the segmentation. Third, side branches in the segmented lumen are removed by applying a shape constraint to the envelope of the maximum inscribed spheres of the segmentation. From the segmented lumen, the authors detect and quantify the cross-sectional area-based and cross-sectional diameter-based stenosis degrees according to the North American Symptomatic Carotid En-darterectomy Trial criterion. Results: The method is trained and tested on a publicly available database from the cls2009 challenge. For the segmentation, the authors obtain a Dice similarity coefficient of 90.2% and a mean absolute surface distance of 0.34 mm. For the stenosis quantification, the authors obtain an average error of 15.7% for cross-sectional diameter-based stenosis and 19.2% for cross-sectional area-based stenosis quantification. Conclusions: With these results, the method ranks second in terms of carotid lumen segmentation accuracy, and first in terms of carotid artery stenosis quantification.

In this paper we present methods for characterizing CCD cameras. Interesting properties are linearity of photometric response, signal-to-noise ratio, sensitivity, dark current, and spatial frequency response. The techniques to characterize CCD cameras are carefully designed to assist one in selecting a camera to solve a certain problem. The methods described were applied to a variety of cameras: an Astromed TE3/A with P86000 chip, a Photometrics CC200 series with Thompson chip TH7882, a Photometrics CC200 series with Kodak chip KAF1400, a Xillix' Micro Imager 1400 with Kodak chip KAF1400, an HCS MXR CCD with a Philips chip and a Sony XC-77RRCE.

Efficient design of multi-component food products containing dry and wet components such as biscuits with a moist fruit filling, is of growing interests for food industry. Technology is needed to prevent or reduce water migration from the moist filling to the dry porous cereal material. This can be done by using moisture barrier systems. Knowledge of the microstructure and its relation to water mobility is necessary to develop stable products. This paper describes a study that uses X-ray microtomography (μCT) for the characterisation and visualisation of the 3-D structure of crackers with different porosity, coated biscuit shells and soup inclusions. μCT was used for imaging the inner cellular structure of the cereal matrix or to analyse the integrity of moisture barriers applied on the cereal product. 3-D image analysis methods were developed to obtain quantitative information about the cellular matrix which can be used as input for simulation models for moisture migration. The developed 3-D image analysis method maps the open cellular structure onto a network (graph) representation in which the nodes correspond to the pores and the vertices to the pore-topore interconnection. The pores (nodes) have properties such as volume, surface area and location whereas the vertices have properties such as direct (open connection) and indirect (separated by a single lamella) area. To check the segmentation and network description a model for pore to pore resistance was used. The obtained results demonstrate the potential of μCT and 3-D image analysis for extracting structural information which can be used in models for the moisture penetration in a cellular bakery product.

Super-resolution restoration is the problem of restoring a high-resolution scene from multiple degraded low-resolution images under motion. Due to imaging blur and noise, this problem is ill-posed. Additional constraints such as smoothness of the solution via regularization is often required to obtain a stable solution. While adding a regularization term to the cost function is a standard practice in image restoration, we propose a restoration algorithm that does not require this extra regularization term. The robustness of the algorithm is achieved by a Gaussian-weighted L2-norm in the data mis¯t term that does not response to intensity outliers. With the outliers suppressed, our solution behaves similarly to a maximum-likelihood solution in the presence of Gaussian noise. The e®ectiveness of our algorithm is demonstrated with super-resolution restoration of real infrared image sequences under severe aliasing and intensity outliers.

Defocus and twofold astigmatism are the key parameters governing the contrast transfer function (CTF) in transmission electron microscopy (TEM) of weak phase objects. We present a new algorithm to estimate these aberrations and the associated uncertainties. Tests show very good agreement between simulated and estimated defocus and astigmatism. We evaluate the reproducibility of the algorithm on experimental data by repeating measurements of an amorphous sample under identical imaging conditions and by analyzing the linearity of the stigmator response. By using a new Thon ring averaging method, the modulation depth of the rings in a 1D averaged power spectrum density (PSD) can be enhanced compared to elliptical averaging. This facilitates a better contrast transfer assessment in the presence of spherical aberration. Our algorithm for defocus and astigmatism estimation inverts the contrast of the Thon rings and suppresses the background in the PSD using an adaptive filtering strategy. Template matching with kernels of various ellipticities is applied to the filtered PSD after transformation into polar coordinates. Maxima in the resulting 3D parameter space provide multiple estimates of the long axis orientation, frequencies and apparent ellipticities of the rings. The frequencies of the detected rings, together with outlier rejection and assignment of an order to the CTF zeros, are used to estimate the defocus and its uncertainty. From estimations of defocus and ellipticity, we derive astigmatism and its uncertainty. A two-pass approach refines the astigmatism and defocus estimate by taking into account the influence of the known spherical aberration on the shape and frequencies of the rings. The implementation of the presented algorithm is freely available for non-commercial use.

We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the traditional framework, however, the window function of adaptive NC is adapted to local linear structures. This leads to more samples of the same modality being gathered for the analysis, which in turn improves signal-to-noise ratio and reduces diffusion across discontinuities. A robust signal certainty is also adapted to the sample intensities to minimize the influence of outliers. Excellent fusion capability of adaptive NC is demonstrated through an application of super-resolution image reconstruction.

Virtual colonoscopy is a non-invasive technique for the detection of polyps. Currently, a clean colon is required; as without cleansing the colonic wall cannot be segmented. Enhanced bowel preparation schemes opacify intraluminal remains to enable colon segmentation. Computed cleansing (as opposed to physical cleansing of the bowels) allows removal of tagged intraluminal remains. This paper describes a model that allows proper classification of transitions between three materials: gas, tissue and tagged intraluminal remains. The computed cleansing effectively detects and removes the remains from the data. Inspection of the ‘clean’ wall is possible using common surface visualization techniques.

We propose a novel technique for tract-based comparison of DTI-indices between groups, based on a representation that is estimated while matching fiber tracts. The method involves a non-rigid registration based on a joint clustering and matching approach, after which a 3D-atlas of cluster center points is used as a frame of reference for statistics. Patient and control FA-distributions are compared per cluster. Spatial consistency is taken to reflect a significant difference between groups. Accordingly, a non-parametric classification is performed to assess the continuity of pathology over larger tract regions. In a study to infant survivors treated for medulloblastoma with intravenous methotrexate and cranial radiotherapy, significant decreases in FA in major parts of the corpus callosum were found.