Source Camera Verification from Strongly Stabilized Videos

Image stabilization performed during imaging and/or post-processing poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos. When performed digitally, stabilization involves cropping, warping, and inpainting of video frames to eliminate unwanted camera motion. Hence, successful attribution requires the inversion of these transformations in a blind manner. To address this challenge, we introduce a source camera verification method for videos that takes into account the spatially variant nature of stabilization transformations and assumes a larger degree of freedom in their search. Our method identifies transformations at a sub-frame level, incorporates a number of constraints to validate their correctness, and offers computational flexibility in the search for the correct transformation. The method also adopts a holistic approach in countering disruptive effects of other video generation steps, such as video coding and downsizing, for more reliable attribution. Tests performed on one public and two custom datasets show that the proposed method is able to verify the source of 23-30% of all videos that underwent stronger stabilization, depending on computation load, without a significant impact on false attribution

Versatile Surface Electrodes for Combined Electrophysiology and Two-Photon Imaging of the Mouse Central Nervous System

Understanding and modulating CNS function in physiological as well as pathophysiological contexts remains a significant ambition in research and clinical applications. The investigation of the multifaceted CNS cell types including their interactions and contributions to neural function requires a combination of the state-ofthe-art in vivo electrophysiology and imaging techniques. We developed a novel type of liquid crystal polymer (LCP) surface micro-electrode manufactured in three customized designs with up to 16 channels for recording and stimulation of brain activity. All designs include spare central spaces for simultaneous 2P-imaging. Nanoporous platinumplated contact sites ensure a low impedance and high current transfer. The epidural implantation of the LCP micro-electrodes could be combined with standard cranial window surgery. The epidurally positioned electrodes did not only display long-term biocompatibility, but we also observed an additional stabilization of the underlying CNS tissue. We demonstrate the electrode’s versatility in combination with in vivo 2P-imaging by monitoring anesthesia-awake cycles of transgenic mice with GCaMP3 expression in neurons or astrocytes. Cortical stimulation and simultaneous 2P Ca2+ imaging in neurons or astrocytes highlighted the astrocytes’ integrative character in neuronal activity processing. Furthermore, we confirmed that spontaneous astroglial Ca2+ signals are dampened under anesthesia, while evoked signals in neurons and astrocytes showed stronger dependency on stimulation intensity rather than on various levels of anesthesia. Finally, we show that the electrodes provide recordings of the electrocorticogram (ECoG) with a high signal-to noise ratio and spatial signal differences which help to decipher brain activity states during experimental procedures. Summarizing, the novel LCP surface micro-electrode is a versatile, convenient, and reliable tool to investigate brain function in vivo

High-quality, high-throughput measurement of protein-DNA binding using HiTS-FLIP

In order to understand in more depth and on a genome wide scale the behavior of transcription factors (TFs), novel quantitative experiments with high-throughput are needed. Recently, HiTS-FLIP (High-Throughput Sequencing-Fluorescent Ligand Interaction Profiling) was invented by the Burge lab at the MIT (Nutiu et al. (2011)). Based on an Illumina GA-IIx machine for next-generation sequencing, HiTS-FLIP allows to measure the affinity of fluorescent labeled proteins to millions of DNA clusters at equilibrium in an unbiased and untargeted way examining the entire sequence space by Determination of dissociation constants (Kds) for all 12-mer DNA motifs. During my PhD I helped to improve the experimental design of this method to allow measuring the protein-DNA binding events at equilibrium omitting any washing step by utilizing the TIRF (Total Internal Reflection Fluorescence) based optics of the GA-IIx. In addition, I developed the first versions of XML based controlling software that automates the measurement procedure. Meeting the needs for processing the vast amount of data produced by each run, I developed a sophisticated, high performance software pipeline that locates DNA clusters, normalizes and extracts the fluorescent signals. Moreover, cluster contained k-mer motifs are ranked and their DNA binding affinities are quantified with high accuracy. My approach of applying phase-correlation to estimate the relative translative Offset between the observed tile images and the template images omits resequencing and thus allows to reuse the flow cell for several HiTS-FLIP experiments, which greatly reduces cost and time. Instead of using information from the sequencing images like Nutiu et al. (2011) for normalizing the cluster intensities which introduces a nucleotide specific bias, I estimate the cluster related normalization factors directly from the protein Images which captures the non-even illumination bias more accurately and leads to an improved correction for each tile image. My analysis of the ranking algorithm by Nutiu et al. (2011) has revealed that it is unable to rank all measured k-mers. Discarding all the clusters related to previously ranked k-mers has the side effect of eliminating any clusters on which k-mers could be ranked that share submotifs with previously ranked k-mers. This shortcoming affects even strong binding k-mers with only one mutation away from the top ranked k-mer. My findings show that omitting the cluster deletion step in the ranking process overcomes this limitation and allows to rank the full spectrum of all possible k-mers. In addition, the performance of the ranking algorithm is drastically reduced by my insight from a quadratic to a linear run time. The experimental improvements combined with the sophisticated processing of the data has led to a very high accuracy of the HiTS-FLIP dissociation constants (Kds) comparable to the Kds measured by the very sensitive HiP-FA assay (Jung et al. (2015)). However, experimentally HiTS-FLIP is a very challenging assay. In total, eight HiTS-FLIP experiments were performed but only one showed saturation, the others exhibited Protein aggregation occurring at the amplified DNA clusters. This biochemical issue could not be remedied. As example TF for studying the details of HiTS-FLIP, GCN4 was chosen which is a dimeric, basic leucine zipper TF and which acts as the master regulator of the amino acid starvation Response in Saccharomyces cerevisiae (Natarajan et al. (2001)). The fluorescent dye was mOrange. The HiTS-FLIP Kds for the TF GCN4 were validated by the HiP-FA assay and a Pearson correlation coefficient of R=0.99 and a relative error of delta=30.91% was achieved. Thus, a unique and comprehensive data set of utmost quantitative precision was obtained that allowed to study the complex binding behavior of GCN4 in a new way. My Downstream analyses reveal that the known 7-mer consensus motif of GCN4, which is TGACTCA, is modulated by its 2-mer neighboring flanking regions spanning an affinity range over two orders of magnitude from a Kd=1.56 nM to Kd=552.51 nM. These results suggest that the common 9-mer PWM (Position Weight Matrix) for GCN4 is insufficient to describe the binding behavior of GCN4. Rather, an additional left and right flanking nucleotide is required to extend the 9-mer to an 11-mer. My analyses regarding mutations and related delta delta G values suggest long-range interdependencies between nucleotides of the two dimeric half-sites of GCN4. Consequently, models assuming positional independence, such as a PWM, are insufficient to explain these interdependencies. Instead, the full spectrum of affinity values for all k-mers of appropriate size should be measured and applied in further analyses as proposed by Nutiu et al. (2011). Another discovery were new binding motifs of GCN4, which can only be detected with a method like HiTS-FLIP that examines the entire sequence space and allows for unbiased, de-novo motif discovery. All These new motifs contain GTGT as a submotif and the data collected suggests that GCN4 binds as monomer to these new motifs. Therefore, it might be even possible to detect different binding modes with HiTS-FLIP. My results emphasize the binding complexity of GCN4 and demonstrate the advantage of HiTS-FLIP for investigating the complexity of regulative processes

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

Biorthogonality in lapped transforms : a study in high-quality audio compression

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 76-82).by Shiufun Cheung.Ph.D

MR Shuffling: Accelerated Single-Scan Multi-Contrast Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is an attractive medical imaging modality as it is non-invasive and does not involve ionizing radiation. Routine clinical MRI exams obtain MR images corresponding to different soft tissue contrast by performing multiple scans. When two-dimensional (2D) imaging is used, these scans are often repeated in other scanning planes. As a result, the number of scans comprising an MRI exam leads to prohibitively long exam times as compared to other medical imaging modalities such as computed tomography. Many approaches have been designed to accelerate the MRI acquisition while maintaining diagnostic quality.One approach is to collect multiple measurements while the MRI signal is evolving due to relaxation. This enables a reduction in scan time, as fewer acquisition windows are needed to collect the same number of measurements. However, when the temporal aspect of the acquisition is left unmodeled, artifacts are likely to appear in the reconstruction. Most often, these artifacts manifest as image blurring. The effect depends on the acquisition parameters as well as the tissue relaxation itself, resulting in spatially varying blurring. The severity of the artifacts is directly related to the level of acceleration, and thus presents a tradeoff with scan time. The effect is amplified when imaging in three dimensions, severely limiting scan efficiency. Volumetric variants would be used if not for the blurring, as they are able to reconstruct images at isotropic resolution and support mutli-planar reformatting.Another established acceleration technique, called parallel imaging, takes advantage of spatially sensitive receive coil arrays to collect multiple MRI measurements in parallel. Thus, the acquisition is shortened, and the reconstruction uses the spatial sensitivity information to recover the image. More recently, methods have been developed that leverage image structure such as sparsity and low rank to reduce the required number of samples for a well-posed reconstruction. Compressed sensing and its low rank extensions use these concepts to acquire incoherent measurements below the Nyquist rate. These techniques are especially suited to MRI, as incoherent measurements can be easily achieved through pseudo-random under-sampling. As the mechanisms behind parallel imaging and compressed sensing are fundamentally different, they can be combined to achieve even higher acceleration.This dissertation proposes accelerated MRI acquisition and reconstruction techniques that account for the temporal dynamics of the MR signal. The methods build off of parallel imaging and compressed sensing to reduce scan time and flexibly model the temporal relaxation behavior. By randomly shuffling the sampling in the acquisition stage and imposing low rank constraints in the reconstruction stage, intrinsic physical parameters are modeled and their dynamics are recovered as multiple images of varying tissue contrast. Additionally, blurring artifacts are significantly reduced, as the temporal dynamics are accounted for in the reconstruction.This dissertation first introduces T2 Shuffling, a volumetric technique that reduces blurring and reconstructs multiple T2-weighted image contrasts from a single acquisition. The method is integrated into a clinical hospital environment and evaluated on patients. Next, this dissertation develops a fast and distributed reconstruction for T2 Shuffling that achieves clinically relevant processing time latency. Clinical validation results are shown comparing T2 Shuffling as a single-sequence alternative to conventional pediatric knee MRI. Based off the compelling results, a fast targeted knee MRI using T2 Shuffling is implemented, enabling same-day access to MRI at one-third the cost compared to the conventional exam. To date, over 2,400 T2 Shuffling patient scans have been performed.Continuing the theme of accelerated multi-contrast imaging, this dissertation extends the temporal signal model with T1-T2 Shuffling. Building off of T2 Shuffling, the new method additionally samples multiple points along the saturation recovery curve by varying the repetition time durations during the scan. Since the signal dynamics are governed by both T1 recovery and T2 relaxation, the reconstruction captures information about both intrinsic tissue parameters. As a result, multiple target synthetic contrast images are reconstructed, all from a single scan. Approaches for selecting the sequence parameters are provided, and the method is evaluated on in vivo brain imaging of a volunteer.Altogether, these methods comprise the theme of MR Shuffling, and may open new pathways toward fast clinical MRI

Wearable electroencephalography for long-term monitoring and diagnostic purposes

Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces