An Efficient Digital Image Watermarking Based on DCT and Advanced Image Data Embedding Method

Digital image enhancement and digital content or data image secure using DCT and advanced image data embedding method (AIDEM). AIDEM improved robustness based on particle shifting concept is reproduced secure image data and manipulated there’s a robust would like for a digital image copyright mechanism to be placed in secure image data. There’s a necessity for authentication of the content because of the owner. It’s become more accessible for malicious parties to create scalable copies of proprietary content with any compensation to the content owner. Advanced Watermarking is being viewed as a potential goal to the current downside. Astounding watermarking plans are arranged assaults on the watermarked picture are twisted and proposed to give insurance of proprietorship freedoms, information treating, and information uprightness. These methods guarantee unique information recuperation from watermarked information, while irreversible watermarking plans safeguard proprietorship freedoms. This attribute of reversible watermarking has arisen as an applicant answer for the assurance of proprietorship freedoms of information, unfortunate to alterations, for example, clinical information, genetic information, Visa, and financial balance information. These attacks are also intentional or unintentional. The attacks are classified as geometric attacks. This research presents a comprehensive and old method of these techniques that are developed and their effectiveness. Digital watermarking was developed to supply copyright protection and owners’ authentication. Digital image watermarking may be a methodology for embedding some information into digital image sequences, like text image, image data, during this research analysis on image watermarking and attacks on watermarking process time image data, classification of watermarking and applications. We aim to secure image data using advanced image data embedding method (AIDEM) improved robustness based particle shifting concept is reproduced secure image data. To develop compelling digital image watermarking methodology using mat lab tool and reliable and robust

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

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

A multi-frame super-resolution algorithm using pocs and wavelet

Super-Resolution (SR) is a generic term, referring to a series of digital image processing techniques in which a high resolution (HR) image is reconstructed from a set of low resolution (LR) video frames or images. In other words, a HR image is obtained by integrating several LR frames captured from the same scene within a very short period of time. Constructing a SR image is a process that may require a lot of computational resources. To solve this problem, the SR reconstruction process involves 3 steps, namely image registration, degrading function estimation and image restoration. In this thesis, the fundamental process steps in SR image reconstruction algorithms are first introduced. Several known SR image reconstruction approaches are then discussed in detail. These SR reconstruction methods include: (1) traditional interpolation, (2) the frequency domain approach, (3) the inverse back-projection (IBP), (4) the conventional projections onto convex sets (POCS) and (5) regularized inverse optimization. Based on the analysis of some of the existing methods, a Wavelet-based POCS SR image reconstruction method is proposed. The new method is an extension of the conventional POCS method, that performs some convex projection operations in the Wavelet domain. The stochastic Wavelet coefficient refinement technique is used to adjust the Wavelet sub-image coefficients of the estimated HR image according to the stochastic F-distribution in order to eliminate the noisy or wrongly estimated pixels. The proposed SR method enhances the resulting quality of the reconstructed HR image, while retaining the simplicity of the conventional POCS method as well as increasing the convergence speed of POCS iterations. Simulation results show that the proposed Wavelet-based POCS iterative algorithm has led to some distinct features and performance improvement as compared to some of the SR approaches reviewed in this thesis

Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms

The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms. Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep. Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied. A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied. The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image. Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds. Twenty student observers were asked to detect the orientation of the spiculation in the image. There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4. The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved

A beta-herpesvirus with fluorescent capsids to study transport in living cells.

Fluorescent tagging of viral particles by genetic means enables the study of virus dynamics in living cells. However, the study of beta-herpesvirus entry and morphogenesis by this method is currently limited. This is due to the lack of replication competent, capsid-tagged fluorescent viruses. Here, we report on viable recombinant MCMVs carrying ectopic insertions of the small capsid protein (SCP) fused to fluorescent proteins (FPs). The FPs were inserted into an internal position which allowed the production of viable, fluorescently labeled cytomegaloviruses, which replicated with wild type kinetics in cell culture. Fluorescent particles were readily detectable by several methods. Moreover, in a spread assay, labeled capsids accumulated around the nucleus of the newly infected cells without any detectable viral gene expression suggesting normal entry and particle trafficking. These recombinants were used to record particle dynamics by live-cell microscopy during MCMV egress with high spatial as well as temporal resolution. From the resulting tracks we obtained not only mean track velocities but also their mean square displacements and diffusion coefficients. With this key information, we were able to describe particle behavior at high detail and discriminate between particle tracks exhibiting directed movement and tracks in which particles exhibited free or anomalous diffusion

Label-Free Characterization of Single Proteins Using Synthetic Nanopores

Molecular diagnosis has proven to be a powerful tool for early detection of neurodegenerative disease, but research in this field is still relatively nascent. In Alzheimer's Disease specifically, levels of microtubule associated protein tau and amyloid beta in cerebrospinal fluid are becoming reliable pathological indicators. The current gold standard for detecting these biomarkers is an enzyme-linked immunosorbent assay, and while this method has a limit of detection on the order of picograms per mL, it lacks the ability to provide information about aggregation extent and structure on a per-protein basis. From a disease standpoint, neurological pathologies are often extremely complex in their biological manifestation, and precise mechanisms for many of these diseases are still being discovered and revised. A thorough understanding of in situ structure and properties of neurological disease-related proteins would likely help clarify some of these complicated mechanisms. Resistive-pulse methods may be useful in this effort, as they can determine specific biomarker concentrations and can also unveil multiple physical qualities of single proteins or protein aggregates in an aqueous sample. The latter capability is critical, and could allow for both earlier diagnoses and a stronger mechanistic understanding of neurological disease progression. The work presented in this dissertation, therefore, represents broad efforts toward developing a nanopore-based system able to characterize amyloids and protein complexes related to neurodegenerative disease. These efforts range from upstream fabrication and characterization of nanopores in synthetic substrates to downstream techniques for optimizing the accuracy and efficiency of analyses on resistive pulses. Single proteins rotating and translating while tethered to the surface of a nanopore provide rich information during transit through the pore that makes it possible to determine their ellipsoidal shape, volume, dipole moment, charge, and rotational diffusion coefficient in a time frame of just a few hundred microseconds. This five-dimensional protein fingerprint, however, requires chemical modification of each protein and is thus not ideal for studying protein dynamics or transient protein complexes, both of which are relevant when characterizing amyloids. Transitioning to low-noise nanopore substrates and high-bandwidth recordings enables label-free identification and quantification of unperturbed, natively-folded proteins and protein complexes in solution -- no chemical tags, tethers, or fluorescent labels are needed. Such a transition is nontrivial; proteins passing uninhibited through the strong electric field inside of a nanopore rotate and translocate rapidly, posing a challenge to time-resolve their various orientations adequately while circumventing adhesion to nanopore walls. Furthermore, during their translocation through the nanopore, untethered, native proteins diffuse laterally, generating asymmetric disturbances of the electric field and larger-than-expected resistive pulse magnitudes. Known as off-axis effects, these latter phenomena add a noise-like element to the electrical recordings. We evaluate, both computationally and experimentally, the influence of such label-free complications on resulting parameter estimates, and place these results in the context of developing future iterations of nanopore-based protein sensors. In light of the spectacular recent success of nanopore-based nucleic acid sequencing, it is likely that the next frontier for nanopore-based analysis is the characterization of single proteins and, in particular, the characterization of protein aggregates such as amyloids. The experiments and results presented here enable future particle-by-particle analysis of amyloids with nanopores to rapidly reconstruct their heterogeneity in size and shape, both of which are correlated with the neurotoxicity of amyloid samples and are being investigated as biomarkers for neurodegenerative diseases.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/151730/1/jaredsh_1.pdfDescription of jaredsh_1.pdf : Restricted to UM users only

Design, tuning and performance evaluation of an automated pulmonary nodule detection system

Radiologists miss about 25-30% of all pulmonary nodules smaller than 1.0 cm. in mass screenings. A system for the automated detection of the pulmonary nodule based on that of Hallard has been designed, tuned, and tested on a 43 chest radiographs [Ballard, 1973). The goal of this system is to aid the radiologist in locating a pulmonary nodule by indicating a few sites in the radiograph that are most likely to be nodules. Computer image analysis programs that respond to specific types of anatomic features have been devised and are incorporated in a pattern recognizer, which uses linear discriminant analysis to classify the candidate nodule sites. Candidate nodule sites that are not classified as nodules are eliminated from the list of sites that are presented to the radiologist for inspection. The pattern recognizer was trained with the features from 2750 candidate nodules, which came from 37 films and another pattern recognizer was trained with the features from 402 candidate nodules from 9 films. This research demonstrates that pattern recognition techniques and procedurally driven image experts are capable of reducing the number of candidate nodule sites that a radiologist must inspect from at most 12 to at most 4 if he is to be 99% confident of having inspected any nodule detected by the system which was trained with 37 films. The radiologist must be willing to accept a film true positive rate of 88% (as opposed to a film true positive rate of 92%) for the convenience of having fewer points to inspect. These film true positive rates are derived from 37 films which contain nodules that were evaluated by the system. The particular contributions of this work lies in the implementation and testing of a spline filter, a preprocessing step, which removes background variations in the radiograph so that nodules are more visible; the development of Vascularity and Rib Experts which recognize these classes of candidate nodules; and in die implementation of the particular features that are extracted from the candidate nodule and used by the pattern classifier