Robust inversion and detection techniques for improved imaging performance

Thesis (Ph.D.)--Boston UniversityIn this thesis we aim to improve the performance of information extraction from imaging systems through three thrusts. First, we develop improved image formation methods for physics-based, complex-valued sensing problems. We propose a regularized inversion method that incorporates prior information about the underlying field into the inversion framework for ultrasound imaging. We use experimental ultrasound data to compute inversion results with the proposed formulation and compare it with conventional inversion techniques to show the robustness of the proposed technique to loss of data. Second, we propose methods that combine inversion and detection in a unified framework to improve imaging performance. This framework is applicable for cases where the underlying field is label-based such that each pixel of the underlying field can only assume values from a discrete, limited set. We consider this unified framework in the context of combinatorial optimization and propose graph-cut based methods that would result in label-based images, thereby eliminating the need for a separate detection step. Finally, we propose a robust method of object detection from microscopic nanoparticle images. In particular, we focus on a portable, low cost interferometric imaging platform and propose robust detection algorithms using tools from computer vision. We model the electromagnetic image formation process and use this model to create an enhanced detection technique. The effectiveness of the proposed technique is demonstrated using manually labeled ground-truth data. In addition, we extend these tools to develop a detection based autofocusing algorithm tailored for the high numerical aperture interferometric microscope

Sparsity driven ultrasound imaging

An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. The framework involves the use of a physics-based forward model of the ultrasound observation process, the formulation of image formation as the solution of an associated optimization problem, and the solution of that problem through efficient numerical algorithms. The sparsity-driven, model-based approach estimates a complex-valued reflectivity field and preserves physical features in the scene while suppressing spurious artifacts. It also provides robust reconstructions in the case of sparse and reduced observation apertures. The effectiveness of the proposed imaging strategy is demonstrated using experimental data

Gözetim videolarında nesne ve olay tanımlama başarım analizi için veritabanı oluşturulması

In our era, surveillance systems are largely employed in the field of security and data gathering. The main driving force behind the expansion of these visual surveillance systems is due to the active use of visual surveillance systems in security applications. For these systems to be developed and to be able to act in real time, the performance of object and event detection algorithms must be improved. The objective comparison of detection algorithms will provide a concrete base to carry out research on this topic and lead to the measurement of real developments. To be able to conduct objective comparisons, databases which are accessible for research purposes and evaluation metrics are needed. Within the literature itself, a few evaluation metrics are defined, but databases that are accessible for research purposes are not common. This paper presents an analysis of a database which was formed within Sabancı University, based on surveillance systems’ use of object and detection algorithms. Finally the performance analysis of an object detection algorithm that was tested on the database is presented

Single Nanoparticle Detection for Multiplexed Protein Diagnostics with Attomolar Sensitivity in Serum and Unprocessed Whole Blood

Although biomarkers exist for a range of disease diagnostics, a single low-cost platform exhibiting the required sensitivity, a large dynamic-range and multiplexing capability, and zero sample preparation remains in high demand for a variety of clinical applications. The Interferometric Reflectance Imaging Sensor (IRIS) was utilized to digitally detect and size single gold nanoparticles to identify protein biomarkers in unprocessed serum and blood samples. IRIS is a simple, inexpensive, multiplexed, high-throughput, and label-free optical biosensor that was originally used to quantify biomass captured on a surface with moderate sensitivity. Here we demonstrate detection of β-lactoglobulin, a cow’s milk whey protein spiked in serum (>10 orders of magnitude) and whole blood (>5 orders of magnitude), at attomolar sensitivity. The clinical utility of IRIS was demonstrated by detecting allergen-specific IgE from microliters of characterized human serum and unprocessed whole blood samples by using secondary antibodies against human IgE labeled with 40 nm gold nanoparticles. To the best of our knowledge, this level of sensitivity over a large dynamic range has not been previously demonstrated.IRIS offers four main advantages compared to existing technologies: it (i) detects proteins from attomolar to nanomolar concentrations in unprocessed biological samples, (ii) unambiguously discriminates nanoparticles tags on a robust and physically large sensor area, (iii) detects protein targets with conjugated very small nanoparticle tags (∼40 nm diameter), which minimally affect assay kinetics compared to conventional microparticle tagging methods, and (iv) utilizes components that make the instrument inexpensive, robust, and portable. These features make IRIS an ideal candidate for clinical and diagnostic applications