Weak Lensing Analysis of the z~0.8 cluster CL 0152-1357 with the Advanced Camera for Surveys

We present a weak lensing analysis of the X-ray luminous cluster CL 0152-1357 at z~0.84 using HST/ACS observations. The unparalleled resolution and sensitivity of ACS enable us to measure weakly distorted, faint background galaxies to the extent that the number density reaches ~175 arcmin^-2. The PSF of ACS has a complicated shape that also varies across the field. We construct a PSF model for ACS from an extensive investigation of 47 Tuc stars in a modestly crowded region. We show that this model PSF excellently describes the PSF variation pattern in the cluster observation when a slight adjustment of ellipticity is applied. The high number density of source galaxies and the accurate removal of the PSF effect through moment-based deconvolution allow us to restore the dark matter distribution of the cluster in great detail. The direct comparison of the mass map with the X-ray morphology from Chandra observations shows that the two peaks of intracluster medium traced by X-ray emission are lagging behind the corresponding dark matter clumps, indicative of an on-going merger. The overall mass profile of the cluster can be well described by an NFW profile with a scale radius of r_s =309+-45 kpc and a concentration parameter of c=3.7+-0.5. The mass estimates from the lensing analysis are consistent with those from X-ray and Sunyaev-Zeldovich analyses. The predicted velocity dispersion is also in good agreement with the spectroscopic measurement from VLT observations. In the adopted WMAP cosmology, the total projected mass and the mass-to-light ratio within 1 Mpc are estimated to be 4.92+-0.44 10^14 solar mass and 95+-8 solar mass/solar luminosity, respectively.Comment: Accepted for publication in Astrophysical Journal. 58 pages, 26 figures. Figures have been degraded to meet size limit; a higher resolution version available at http://acs.pha.jhu.edu/~mkjee/ms_cl0152.pd

Curvilinear Structure Enhancement in Biomedical Images

Curvilinear structures can appear in many different areas and at a variety of scales. They can be axons and dendrites in the brain, blood vessels in the fundus, streets, rivers or fractures in buildings, and others. So, it is essential to study curvilinear structures in many fields such as neuroscience, biology, and cartography regarding image processing. Image processing is an important field for the help to aid in biomedical imaging especially the diagnosing the disease. Image enhancement is the early step of image analysis. In this thesis, I focus on the research, development, implementation, and validation of 2D and 3D curvilinear structure enhancement methods, recently established. The proposed methods are based on phase congruency, mathematical morphology, and tensor representation concepts. First, I have introduced a 3D contrast independent phase congruency-based enhancement approach. The obtained results demonstrate the proposed approach is robust against the contrast variations in 3D biomedical images. Second, I have proposed a new mathematical morphology-based approach called the bowler-hat transform. In this approach, I have combined the mathematical morphology with a local tensor representation of curvilinear structures in images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. Especially the proposed method is quite successful while enhancing of curvilinear structures at junctions. Finally, I have extended the bowler-hat approach to the 3D version to prove the applicability, reliability, and ability of it in 3D

Automatic solar feature detection using image processing and pattern recognition techniques

The objective of the research in this dissertation is to develop a software system to automatically detect and characterize solar flares, filaments and Corona Mass Ejections (CMEs), the core of so-called solar activity. These tools will assist us to predict space weather caused by violent solar activity. Image processing and pattern recognition techniques are applied to this system. For automatic flare detection, the advanced pattern recognition techniques such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM) are used. By tracking the entire process of flares, the motion properties of two-ribbon flares are derived automatically. In the applications of the solar filament detection, the Stabilized Inverse Diffusion Equation (SIDE) is used to enhance and sharpen filaments; a new method for automatic threshold selection is proposed to extract filaments from background; an SVM classifier with nine input features is used to differentiate between sunspots and filaments. Once a filament is identified, morphological thinning, pruning, and adaptive edge linking methods are applied to determine filament properties. Furthermore, a filament matching method is proposed to detect filament disappearance. The automatic detection and characterization of flares and filaments have been successfully applied on Hα full-disk images that are continuously obtained at Big Bear Solar Observatory (BBSO). For automatically detecting and classifying CMEs, the image enhancement, segmentation, and pattern recognition techniques are applied to Large Angle Spectrometric Coronagraph (LASCO) C2 and C3 images. The processed LASCO and BBSO images are saved to file archive, and the physical properties of detected solar features such as intensity and speed are recorded in our database. Researchers are able to access the solar feature database and analyze the solar data efficiently and effectively. The detection and characterization system greatly improves the ability to monitor the evolution of solar events and has potential to be used to predict the space weather

Emergent behavior of soil fungal dynamics:influence of soil architecture and water distribution

Macroscopic measurements and observations in two-dimensional soil-thin sections indicate that fungal hyphae invade preferentially the larger, air-filled pores in soils. This suggests that the architecture of soils and the microscale distribution of water are likely to influence significantly the dynamics of fungal growth. Unfortunately, techniques are lacking at present to verify this hypothesis experimentally, and as a result, factors that control fungal growth in soils remain poorly understood. Nevertheless, to design appropriate experiments later on, it is useful to indirectly obtain estimates of the effects involved. Such estimates can be obtained via simulation, based on detailed micron-scale X-ray computed tomography information about the soil pore geometry. In this context, this article reports on a series of simulations resulting from the combination of an individual-based fungal growth model, describing in detail the physiological processes involved in fungal growth, and of a Lattice Boltzmann model used to predict the distribution of air-liquid interfaces in soils. Three soil samples with contrasting properties were used as test cases. Several quantitative parameters, including Minkowski functionals, were used to characterize the geometry of pores, air-water interfaces, and fungal hyphae. Simulation results show that the water distribution in the soils is affected more by the pore size distribution than by the porosity of the soils. The presence of water decreased the colonization efficiency of the fungi, as evinced by a decline in the magnitude of all fungal biomass functional measures, in all three samples. The architecture of the soils and water distribution had an effect on the general morphology of the hyphal network, with a "looped" configuration in one soil, due to growing around water droplets. These morphologic differences are satisfactorily discriminated by the Minkowski functionals, applied to the fungal biomass

Biophotonic Tools in Cell and Tissue Diagnostics.

In order to maintain the rapid advance of biophotonics in the U.S. and enhance our competitiveness worldwide, key measurement tools must be in place. As part of a wide-reaching effort to improve the U.S. technology base, the National Institute of Standards and Technology sponsored a workshop titled "Biophotonic tools for cell and tissue diagnostics." The workshop focused on diagnostic techniques involving the interaction between biological systems and photons. Through invited presentations by industry representatives and panel discussion, near- and far-term measurement needs were evaluated. As a result of this workshop, this document has been prepared on the measurement tools needed for biophotonic cell and tissue diagnostics. This will become a part of the larger measurement road-mapping effort to be presented to the Nation as an assessment of the U.S. Measurement System. The information will be used to highlight measurement needs to the community and to facilitate solutions

Medical image enhancement

Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, “deblurring” an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards