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Whole-brain Dynamics of Neuronal Circuits Enabled by Sculpted Light and Light Field Microscopy

Whole-brain Dynamics of Neuronal Circuits Enabled by Sculpte...

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International IEEE/EMBS conference on Neural Engineering

作者： Tobias Nobauer Robert Prevedel Friederike Schlumm Maximilian Hoffmann Maxim Molodtsov Alipasha vaziri Research Platform for Quantum Phenomena and Nanoscale Biological Systems (QuNaBioS) at University of Vienna

ISBN: (纸本)9781467363877

Capturing the dynamics of neuronal activity across whole nervous systems at high temporal resolution has been a long-standing dream in neuroscience. While point-scanning microscopy methods provide the necessary 3D resolution, their volume acquisition rates are limited. Wide-field microscopes on the other hand do not provide sufficient optical sectioning capability. We recently implemented two complementary fluorescence microscopy methods that allow for simultaneous whole-animal imaging of genetically encoded calcium indicator activity in C. elegans, and whole-brain readout in zebrafish larvae. While Wide-field Temporal Focusing Microscopy "sculpts" the spectral components of femtosecond laser pulses to achieve sectioning, Light Field Deconvolution Microscopy, a tomography related method, uses a microlens array to simultaneously capture spatial and angular information followed by computational reconstruction to acquire volumetric information from a single sensor exposure. Here, we discuss our recent results using both techniques for acquiring whole-brain functional imaging data at speeds up to tens of Hertz and near single cell resolution for small model organisms.

关键词： Microscopy Neurons image resolution image reconstruction Lenses Optical microscopy

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Polygonal Approximation of 2-D Binary images

Polygonal Approximation of 2-D Binary Images

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International conference on Information Technology: New Generations (ITNG)

作者： Seema H. Jadhav B.S. Tarle L.M. Waghmare Department of Instrumentation & Control N. D. M. V. P. SS College of Engineering Nashik India Department of Computer Engineering N. D. M. V. P. SS College of Engineering Nashik India Department of Instrumentation & control S.G.G.S. Institute of Engineering and Technology Vishnupuri India

In many morphological shape decomposition algorithms, either a shape can only be decomposed into shape components of extremely simple forms or a time consuming search process is employed to determine decomposition. In our paper, we present a morphological shape approximation algorithm that decomposes a two dimensional (2D) binary shape into a collection of convex polygonal components. A single convex polygonal approximation for a given image is first identified. This first component is determined incrementally by selecting a sequence of basic shape primitives. These shape primitives are chosen based on shape information extracted from the given shape at different scale levels. Additional shape components are identified recursively from the difference image between the given image and the first component. Simple operations are used to repair certain concavities caused by the set difference operations. After decomposition the original shape can be reconstructed exactly by the union of all convex components and finer details can also be included. Our paper has been implemented using MATLAB software

关键词： Shape control image reconstruction Skeleton Morphology Approximation algorithms Instruments Morphological operations Educational institutions Two dimensional displays data mining

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Effects of attenuation correction on tumor detection in PET oncology

Effects of attenuation correction on tumor detection in PET ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： C. Bai P.E. Kinahan D. Brasse C. Comtat D.W. Townsend C.C. Meltzer v. villemagne M. Charron M. Defrise P.E.T. Facility University of Pittsburgh Medical Center Pittsburgh PA USA Department of Medical Research SHFJ CEA Orsay France Division of Nuclear Medicine AZ-VUB University Hospital Brussels Belgium

In this work the authors studied the effect of attenuation correction on tumor detection in PET oncology through simulation, phantom, and patient studies. images were reconstructed from attenuated projection data with and without performing attenuation correction (AC and non-AC). The simulation and phantom studies showed that when the ratio of the activity concentration in the tumor to that in the torso background was at a critical value, the tumor was invisible in the non-AC image. Above the critical value, the tumor was visible in both AC and non-AC images, while below the critical value, the tumor appeared as a cold spot in non-AC images. Theoretical explanations of these phenomena were approached through examining the attenuated sinograms and the FBP reconstruction without AC. This work suggests that whole-body PET images should not be reconstructed without attenuation correction.

关键词： Attenuation Tumors Positron emission tomography Oncology Neoplasms image reconstruction Torso Lungs Imaging phantoms Lesions

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Deep Learning based image Enhancing Environment with Noise Suppression

Deep Learning based Image Enhancing Environment with Noise S...

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Electronics and Renewable Systems (ICEARS), International conference on

作者： Sivanantham Basireddy Satish Kumar Reddy J. SaiGnaneswar Kalahasti Balaji K. Siva vivek Reddy Kusam Lokesh Reddy Computer Science and Systems Engineering SVEC Tirupati Andhra Pradesh

A deep learning approach will be used to recover ancient pictures that have suffered significant damage. Unlike typical reconstruction processes that are easily handled by supervised learning methods, real-world picture degradation seems to be complex, and the system is unable to generalize due to domain differences between synthetic pictures and actual old pictures. Therefore, using huge amounts of synthetic image pairs combined with real photos, Therefore, using huge amounts of synthetic picture pairs combined with real photos, A unique triplet domain translation network. Two variational autoencoders (vAEs) have been trained to create latent spaces from both fresh and old images, respectively. The translation between two regions is thenmanaged to learn using artificially paired data. This translation normalizes well to actual photographs as the domain gap is filled in the compact latent space. The translation between these two various latent regions has been taught using artificially paired data. This translation normalizes well to images found in the real world because the compact latent space is filled with the domain gap. A global division with an incomplete nonlocal block will target structural issues like cuts and bruises and a local division attacking unstructured defects like unwanted noise and poor contrast to handle the various degradations mixed throughout an old photograph. The latent space fusion of two branches increases the ability to correct numerous flaws in old images. Convolutional neural networks (CNNs) outperform multiple-layer sequenced neural network models at identifying distinct marks, forms, and patterns in images, making them the most efficient method for processing data. The filters are applied by CNN to every pixel in the image. When it comes to visual quality, the suggested method for repairing old photographs performs better than cutting-edge techniques.

关键词： Degradation Deep learning visualization Renewable energy sources Supervised learning Noise reduction Neural networks

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A comparative study between Elliptic Fourier and B-spline descriptors for object contour representation

A comparative study between Elliptic Fourier and B-spline de...

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International image Processing, Applications and Systems conference (IPAS)

作者： Ll. Ferrer-Arnau v. Parisi-Baradad J.A. Soria K. Nasreddine A. Benzinou Department of Electronics Engineering Universitat Politècnica de Catalunya Barcelona Spain Ecole Nationale d'Ingénieurs de Brest (ENIB) UMR CNRS 6285 Lab-STICC France

In this work, a comparative study between Elliptic Fourier and B-spline descriptors is carried out for comparing their efficiency in characterizing the contour shape of image objects. In both cases, the goal is to obtain the least representation error using the fewest possible number of coefficients. With Fourier descriptors, different number of harmonics are used while the remaining ones are set to zero. In the B-spline case, coefficients are obtained iteratively using a least-square filter, followed by a decimation procedure. Linear and cubic B-splines are considered. In general, data will be more compressed when the lower number of coefficients is used, but the representation error also increases considerably. We use a signal/error ratio, expressed in dBs, to measure the similarity of each approximation. The signal value is obtained from the `modulo' addition of all coordinate points, whereas the error value is computed accumulating the `modulo' distance between original and reconstructed shape. It can be shown that for a lower compression rate, the results do not vary significantly in all three methods. For higher compression rates, Elliptic Fourier Descriptors are more efficient than linear and cubic B-splines, especially in soft contours, but B-splines have lower computational cost.

关键词： Splines (mathematics) image coding Shape Approximation methods image reconstruction Finite impulse response filters Harmonic analysis

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Complex domain nonlocal block-matching denoising based on high-order singular value decomposition (HOSvD)

Complex domain nonlocal block-matching denoising based on hi...

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European Signal Processing conference

作者： vladimir Katkovnik Mykola Ponomarenko Karen Egiazarian Laboratory of Signal Processing Tampere University of Technology Tampere Finland

Block matching 3D collaborative filtering (BM3D) is one of the most popular denoising technique based on data sparsity concept applied to specially structured data. In this paper we develop this technique for complex domain, i.e. for application to complex-valued data. Sparsity as an approximation technique can be addressed directly to complex-valued variables or to real-valued pairs phase/amplitude and real/imaginary parts of complex-valued variables. As a result we arrive to various ways of development and obtain a set of quite different algorithms. The algorithms proposed in this paper are composed from two components: nonlocal patch-wise grouping and high-order singular value decomposition (HOSvD) for grouped data processing. The latter gives data adaptive complex-valued bases for complex-valued data or real-valued bases for joint processing of the pairs phase/amplitude, real/imaginary parts of complex-valued variables. Comparative study of the developed algorithms is produced in order to select the most efficient ones.

关键词： Algorithm design and analysis Signal processing algorithms Transforms Three-dimensional displays Noise reduction image reconstruction Extraterrestrial measurements

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FACIAL ACTION UNIT RECOGNITION BY RELATION MODELING from BOTH QUALITATIvE KNOWLEDGE AND QUANTITATIvE data

FACIAL ACTION UNIT RECOGNITION BY RELATION MODELING FROM BOT...

引用

IEEE International conference on Multimedia and Expo Workshops

作者： Zhen Gao Shangfei Wang Chongliang Wu Jun Wang Qiang Ji Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute

ISBN: (纸本)9781479947164

In this paper, we propose to capture Action Unit (AU) relations existing in both qualitative knowledge and quantitative data through Credal Networks (CN). Each node of the CN represents an AU label, and the links and probability intervals capture the probabilistic dependencies among multiple AUs. The structure of CN is designed based on prior knowledge. The parameters of CN are learned from both knowledge and ground-truth AU labels. The AU preliminary estimations are obtained by an existing image-driven recognition method. With the learned credal network, we infer the true AU labels by combining the relationships among labels with the previous obtained estimations. Experimental results on the CK+ database and MMI database demonstrate that with complete AU labels, our CN model is slightly better than the Bayesian Network (BN) model, demonstrating that credal sets learned from data can capture uncertainty more reliably;With incomplete and error-prone AU annotations, our CN model outperforms the BN model, indicating that credal sets can successfully capture qualitative knowledge.

关键词： Prior knowledge Credal network AU recognition

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Discriminative Non-blind Deblurring

Discriminative Non-blind Deblurring

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IEEE conference on Computer vision and Pattern Recognition

作者： Uwe Schmidt Carsten Rother Sebastian Nowozin Jeremy Jancsary Stefan Roth Department of Computer Science TU Darmstadt Microsoft Research Cambridge

ISBN: (纸本)9781467364102

Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake. Even though learning-based deblurring methods exist, they have been limited to the generative case and are computationally expensive. To this date, manually-defined models are thus most widely used, though limiting the attained restoration quality. We address this gap by proposing a discriminative approach for non-blind deblurring. One key challenge is that the blur kernel in use at test time is not known in advance. To address this, we analyze existing approaches that use half-quadratic regularization. from this analysis, we derive a discriminative model cascade for image deblurring. Our cascade model consists of a Gaussian CRF at each stage, based on the recently introduced regression tree fields. We train our model by loss minimization and use synthetically generated blur kernels to generate training data. Our experiments show that the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur.

关键词： generative case computationally expensive real blur deblurring camera shake image blur image reconstruction TESTING TIME integral component

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A system for measurement and visualisation of human back shape for estimation of 3D deformities

A system for measurement and visualisation of human back sha...

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IEEE EMBS International conference on Information Technology Applications in Biomedicine (ITAB)

作者： M. Leonardi A. Rmiie J. Lenarcic Jožef Stefan Institute Ljubljana Slovenia Univerza v Ljubljani Ljubljana SI

Describes a system for the estimation of back deformities, implemented as a software package. We present the methods adopted for measuring, processing and visualisation of human back shape data. Our goal was to develop a system that was adequate for practical clinical work. To achieve this goal, we implemented the applied methodologies in a way that enables the user to explore and understand the results in a user-friendly way, without prior knowledge of technical internals. The programming package performs the following functions: acquisition of data, processing of data, and presentation of the processing results in various forms. The presented and visualised entities support the user in the determination of and judgement about the subject's condition. For the initial determination of the 3D surface model, we employed Fourier transform profilometry (FTP), in which the phase of a projected fringe pattern is extracted, from which the surface shape reconstruction is possible. FTP is often considered to be just one deviation of moire topography, but in FTP, the grating image projected on an object surface is put directly into the computer and processed without using the second grating to generate moire fringes.

关键词： Shape measurement Humans Surface reconstruction Surface topography Gratings Software packages data visualization Functional programming Packaging Fourier transforms

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Machine learning based parametric image estimation for Analyzer-based phase contrast imaging

Machine learning based parametric image estimation for Analy...

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IEEE Nuclear Science Symposium

作者： Oriol Caudevilla Jovan G. Brankov Department of Electrical Engineering Illinois Institute of Technology

ISBN: (纸本)9781479960989

An X-ray beam passing through biological tissue is deflected (i.e., refracted) by a small angle typically <10 μrad. Analyzer-based phase contrast imaging (ABI) systems are capable of measuring this tinny refraction by sampling the intensity of the beam at different propagation directions. An Analyzer crystal is the key element for this task as it acts as a narrow angular filter. Since refraction effects are highly dependent of the radiation wavelength, X-ray beam must be quasi-monochromatic. Therefore the amount of photons that reach the object and detector is much lower then that in traditional radiography. Using a reasonable exposure time, noisy reconstructions of refraction images are obtained. In this manuscript, we present a machine learning parametric image estimation approach to obtain accurate refraction images from noisy raw data.

关键词： Analyzer-Based Imaging Diffraction-Enhanced Imaging Multiple-image Radiography Phase Sensitive Imaging Machine Learning image reconstruction Gaussian Process

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