Jan Švihlík

Biomedical Image Volumes Denoising via the Wavelet Transform

Image denoising represents a crucial initial step in biomedical image processing and analysis. Denoising belongs to the family of image enhancement methods (Bovik, 2009) which comprise also blur reduction, resolution enhancement, artefacts suppression, and edge enhancement. The motivation for enhancing the biomedical image quality is twofold. First, improving the visual quality may yield more accurate medical diagnostics, and second, analytical methods, such as segmentation and content recognition, require image preprocessing on the input. Gradually, noise reduction methods developed in other research fields find their usage in biomedical applications. However, biomedical images, such as images obtained from computed tomography (CT) scanners, are quite specific. Modelling noise based on the first principles of image acquisition and transmission is a too complex task (Borsdorf et al., 2009), and moreover, the noise component characteristics depend on the measurement conditions (Bovik, 2009). Additionally, noise reduction must be carried out with extreme care to avoid suppression of the important image content. For this reason, the results of biomedical image denoising should be consulted with medical experts.

Errata: Estimation of non-Gaussian noise parameters in the wavelet domain using the moment-generating function

We discuss methods for modeling and removal of noise in astronomical images. For its favorable properties, we exploit the undecimated wavelet representation and apply noise suppression in this domain. Usually, the noise analysis of the studied imaging system is carried out in the spatial domain. However, noise in astronomical data is non-Gaussian, and thus the noise model parameters need to be estimated directly in the wavelet domain.We derive equations for estimating the sample moments for non-Gaussian noise in the wavelet domain. We consider that the sample moments in the spatial domain are known from the noise analysis and that the model parameters are estimated by using the method of moments.

Space Variant Point Spread Function Modeling for Astronomical Image Data Processing

This paper deals with evaluation and processing of astronomical image data, which are obtained by WFC (Wide-Field Camera) or UWFC (Ultra Wide-Field Camera) systems. Precision of astronomical image data post-processing and analyzing is very important. Large amount of different kinds of optical aberrations and distortions is included in these systems. The amplitude of wavefront aberration error increases towards margins of the FOV (Field of View). Relation between amount of high order optical aberrations and astrometry measurement precision is discussed in this paper. There are descriptions of the transfer characteristics of astronomical optical systems presented in this paper. Spatially variant (SV) optical aberrations negatively affect the transfer characteristics of all system and make it spatially variant as well. SV model of optical system is presented in this paper. Partially invariant model of optical systems allows using Fourier methods for deconvolution. Some deconvolution results are shown in this paper.

Quality Enhancement in Security Image Information

The image quality of image or video information is a crucial issue in security imaging systems. Our paper deals with three different points - a posteriori image improvement, objective image quality assessment and noise removal. In the first part we have tested and evaluated the impact of edge-enhancing operators. Their performance is of two contradictory effects -edge sharpening and noise level increase. The second part summarizes the ANN application in the objective image quality evaluation procedure. The advanced approach is based upon two advanced methods - mutual information MI and principal component analysis PCA. The third part is devoted to the DWT noise-removal technique and the experimental results are presented

Automated Analysis of Microscopic Images of Isolated Pancreatic Islets.

Clinical islet transplantation programs rely on the capacities of individual centers to quantify isolated islets. Current computer-assisted methods require input from human operators. Here we describe two machine learning algorithms for islet quantification: the trainable islet algorithm (TIA) and the nontrainable purity algorithm (NPA). These algorithms automatically segment pancreatic islets and exocrine tissue on microscopic images in order to count individual islets and calculate islet volume and purity. References for islet counts and volumes were generated by the fully manual segmentation (FMS) method, which was validated against the internal DNA standard. References for islet purity were generated via the expert visual assessment (EVA) method, which was validated against the FMS method. The TIA is intended to automatically evaluate micrographs of isolated islets from future donors after being trained on micrographs from a limited number of past donors. Its training ability was first evaluated on 46 images from four donors. The pixel-to-pixel comparison, binary statistics, and islet DNA concentration indicated that the TIA was successfully trained, regardless of the color differences of the original images. Next, the TIA trained on the four donors was validated on an additional 36 images from nine independent donors. The TIA was fast (67 s/image), correlated very well with the FMS method (R 2 = 1.00 and 0.92 for islet volume and islet count, respectively), and had small REs (0.06 and 0.07 for islet volume and islet count, respectively). Validation of the NPA against the EVA method using 70 images from 12 donors revealed that the NPA had a reasonable speed (69 s/image), had an acceptable RE (0.14), and correlated well with the EVA method (R 2 = 0.88). Our results demonstrate that a fully automated analysis of clinical-grade micrographs of isolated pancreatic islets is feasible. The algorithms described herein will be freely available as a Fiji platform plugin.

Estimation and measurement of space-variant features of imaging systems and influence of this knowledge on accuracy of astronomical measurement

Additional monitoring equipment is commonly used in astronomical imaging. This electro-optical system usually complements the main telescope during acquisition of astronomical phenomena or supports its operation e.g. evaluating the weather conditions. Typically it is a wide-field imaging system, which consists of a digital camera equipped with fish-eye lens. The wide-field imaging system cannot be considered as a space-invariant because of space-variant nature of its input lens. In our previous research efforts we have focused on measurement and analysis of images obtained from the subsidiary all-sky monitor WILLIAM (WIde-field aLL-sky Images Analyzing Monitoring system). Space-variant part of this imaging system consists of input lens with 180 fi angle of view in horizontal and 154 fi in vertical direction. For a precise astronomical measurement over the entire field of view, it is very important to know how the optical aberrations affect characteristics of the imaging system, especially its PSF (Point Spread Function). Two methods were used for characterization of the space-variant PSF, i.e. measurement in the optical laboratory and estimation using acquired images and Zernike polynomials. Analysis of results obtained using these two methods is presented in the paper. Accuracy of astronomical measurements is also discussed while considering the space-variant PSF of the system.

Analysis of images obtained from space-variant astronomical imaging systems

Most of the classical approaches to the measurement and modeling of electro-optical imaging systems rely on the principles of linearity and space invariance (LSI). In our previous research efforts we have focused on measurement and analysis of images obtained from a double station video observation system MAIA (Meteor Automatic Imager and Analyzer). The video acquisition module of this system contains wide-field input lens which contributes to space-variability of the imaging system. For a precise astronomical measurement over the entire field of view, it is very important to comprehend how the characteristics of the imaging system can affect astrometric and photometric outputs. This paper presents an analysis of how the space-variance of the imaging system can affect precision of astrometric and photometric results. This analysis is based on image data acquired in laboratory experiments and astronomical observations with the wide-field system. Methods for efficient calibration of this system to obtain precise astrometric and photometric measurements are also proposed.

Meteor automatic imager and analyzer: current status and preprocessing of image data

In this paper we present current progress in development of new observational instruments for the double station video experiment called MAIA (Meteor Automatic Imager and Analyzer). The main goal of the MAIA project is to monitor activity of the meteor showers and sporadic meteors. This paper presents detailed analysis of imaging parameters based on acquisition of testing video sequences at different light conditions. Among the most important results belong the analysis of opto-electronic conversion function and noise characteristics. Based on these results, requirements for image preprocessing algorithms are proposed.

Application of optimization heuristics for complex astronomical object model identification

Detection and localization of astronomical objects are two of the most fundamental topics in astronomical science where localization uses detection results. Object localization is based on modeling of point spread function and estimation of its parameters. Commonly used models as Gauss or Moffat in objects localization provide good approximation of analyzed objects but cannot be sufficient in the case of exact applications such as object energy estimation. Thus the use of sophisticated models is upon the place. One of the key roles plays also the way of the objective function estimation. The least square method is often used, but it expects data with normal distribution, thus there is a question of a maximum likelihood method application. Another important factor of presented problem is choice of the right optimization method. Classical methods for objective function minimization usually require a good initial estimate for all parameters and differentiation of the objective function with respect to model parameters. The results indicated that stochastic methods such as simulated annealing or harmony search achieved better results than the classical optimization methods.

Meteor automatic imager and analyzer: analysis of noise characteristics and possible noise suppression

This paper is devoted to the noise analysis and noise suppression in a system for double station observation of the meteors now known as MAIA (Meteor Automatic Imager and Analyzer). The noise analysis is based on acquisition of testing video sequences at different light conditions and their further analysis. The main goal is to find a suitable noise model and subsequently determine if the noise is signal dependent or not. Noise and image model in the wavelet domain should be based on Gaussian mixture model (GMM) or Generalized Laplacian Model (GLM) and the model parameters should be estimated by moment method. GMM and GLM allow to model various types of probability density functions. Finally the advanced de-noising algorithm using Bayesian estimator will be applied.