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A review on automatic analysis techniques for color fundus photographs

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2016年 14卷 371-384页

作者： Besenczi, Renato Toth, Janos Hajdu, Andras Univ Debrecen Fac Informat POB 400 H-4002 Debrecen Hungary

In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field. (C) 2015 The Authors. Published by Elsevier B.v. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Biomedical imaging Retinal diseases Fundus image analysis Clinical decision support

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Super-resolution SAR imaging: Optimal algorithm synthesis and simulation results

Super-resolution SAR imaging: Optimal algorithm synthesis an...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： v. F. Kravchenko B. G. Kutuza v. K. volosyuk v. v. Pavlikov S. S. Zhyla Kotelnikov Institute of Radio Engineering and Electronics Russian Academy of Sciences Russia Scientic and Technological Center of Unique Instrumentation Russian Academy of Sciences Russia Bauman Moscow State Technical University Russia National Aerospace University “Kharkiv Aviation Institute” Ukraine

Synthetic aperture radar (SAR) is a widely used technique suited for real-time and all-weather imaging of natural surfaces and artificial objects. To improve image resolution and increase accuracy of estimation parameters the problem of statistical synthesis of signal processing algorithm in SAR is solved. Proposed method allows to form images with super-resolution in azimuth and range. Synthesis is performed using modern theory of radio engineering systems statistical optimization.

关键词： Signal processing algorithms Correlation Synthetic aperture radar Scattering Surface treatment Decorrelation Radar imaging

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image Restoration and Object Removal Using Prioritized Adaptive Patch-Based Inpainting in a Wavelet Domain

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JOURNAL OF INFORMATION processing systems 2017年第5期13卷 1183-1202页

作者： Borole, Rajesh P. Bonde, Sanjiv v. Shri Guru Gobind Singhji Inst Engn & Technol Dept Elect & Telecom Nanded MS India

image restoration has been carried out by texture synthesis mostly for large regions and inpainting algorithms for small cracks in images. In this paper, we propose a new approach that allows for the simultaneous fill-in of different structures and textures by processing in a wavelet domain. A combination of structure inpainting and patch-based texture synthesis is carried out, which is known as patch-based inpainting, for filling and updating the target region. The wavelet transform is used for its very good multiresolution capabilities. The proposed algorithm uses the wavelet domain subbands to resolve the structure and texture components in smooth approximation and high frequency structural details. The subbands are processed separately by the prioritized patch-based inpainting with isophote energy driven texture synthesis at the core. The algorithm automatically estimates the wavelet coefficients of the target regions of various subbands using optimized patches from the surrounding DWT coefficients. The suggested performance improvement drastically improves execution speed over the existing algorithm. The proposed patch optimization strategy improves the quality of the fill. The fill-in is done with higher priority to structures and isophotes arriving at target boundaries. The effectiveness of the algorithm is demonstrated with natural and textured images with varying textural complexions.

关键词： image Inpainting Object Removal Region Filling Texture and Structure Propagation Wavelet Inpainting

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Structural Analysis of Protein Complexes by Cryo Electron Microscopy

Structural Analysis of Protein Complexes by Cryo Electron Mi...

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作者： Tiago R. Costa Athanasios Ignatiou Elena v. Orlova

Structural studies of biocomplexes using single-particle cryo-electron microscopy (cryo-EM) is now a well-established technique in structural biology and has become competitive with X-ray crystallography. The latest advances in EM enable us to determine structures of protein complexes at 3–5 Å resolution for an extremely broad range of sizes from ~200 kDa up to hundreds of megadaltons (Bartesaghi et al., Science 348(6239):1147–1151, 2051; Bai et al., Nature 525(7568):212–217, 2015; vinothkumar et al., Nature 515(7525):80–84, 2014; Grigorieff and Harrison, Curr Opin Struct Biol 21(2):265–273, 2011). The majority of biocomplexes comprise a number of different components and are not amenable to crystallisation. Secretion systems are typical examples of such multi-protein complexes, and structural studies of them are extremely challenging. The only feasible approach to revealing their spatial organisation and functional modification is cryo-EM. The development of systems for digital registration of images and algorithms for the fast and efficient processing of recorded images and subsequent analysis facilitated the determination of structures at near-atomic resolution. In this review we will describe sample preparation for cryo-EM, how data are collected by new detectors, and the logistics of image analysis through the basic steps required for reconstructions of both small and large biological complexes and their refinement to nearly atomic resolution. The processing workflow is illustrated using examples of EM analysis of a Type Iv Secretion System. less

关键词： Cryo-EM Single Particle Analysis image processing Electron Microscopy (EM) X-ray Crystallography Crystallization image Analysis single-particle cryo-electron microscopy Sample preparation Type Iv secretion system

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An Embedded Solution for Multispectral Palmprint Recognition

An Embedded Solution for Multispectral Palmprint Recognition

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European Signal processing Conference

作者： Chao LI Yannick BENEZETH Keisuke NAKAMURA Randy GOMEZ Fan YANG LE2I UMR6306 CNRS Univ. Bourgogne Franche-Comte Dijon France Chinese Academy of Sciences Institute of Acoustics Beijing Chine Honda Research Institute Japan Japan

Palmprint based identification has attracted much attention in the past decades. In some real-life applications, portable personal authentication systems with high accuracy and speed efficiency are required. This paper presents an embedded palmprint recognition solution based on the multispectral image modality. We first develop an effective recognition algorithm by using partial least squares regression, then a FPGA prototype is implemented and optimized through high-level synthesis technique. The evaluation experiments demonstrate that the proposed system can achieve a higher recognition rate at a lower running cost comparing to the reference implementations.

关键词： Signal processing algorithms Optimization Algorithm design and analysis Feature extraction Databases Pipelines

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A new framework for remote sensing image super-resolution: Sparse representation-based method by processing dictionaries with multi-type features

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JOURNAL OF systems ARCHITECTURE 2016年 64卷 63-75页

作者： Wu, Wei Yang, Xiaomin Liu, Kai Liu, Yiguang Yan, Binyu Hua, Hua Sichuan Univ Coll Elect & Informat Engn Chengdu 610064 Sichuan Peoples R China Sichuan Univ Sch Elect Engn & Informat Chengdu 610064 Sichuan Peoples R China

Remote sensing images play an important role in many practical applications, however, due to the physical limitations of remote sensing devices, it is difficult to obtain images at an expecting high resolution level. Acquiring high-resolution(HR) images from the original low-resolution(LR) ones with super-resolution(SR) methods has always been an attractive proposition in embedded systems including various kinds of tablet PC and smart phone. SR methods based on sparse representation have been successfully used in processing remote sensing images, however, they have two major problems in common. First, they use only one type of image features to represent the low resolution(LR) images. However, one single type of features cannot accurately represent an image due to the diverse structures of the image, as a result, artifacts would be produced simultaneously. Second, many dictionary learning methods try to build a universal dictionary with only one single type of features. However, apparently, a dictionary with a single type of features is not enough to capture the different structures of a remote sensing image, without any doubt, the resultant image would turn out to be a poor one. To overcome the problems above, we propose a new framework for remote sensing image super resolution: sparse representation-based SR method by processing dictionaries with multi-type features. First, in order to represent the remote sensing image more accurately, different types of features are extracted from images. Second, to achieve a better performance, various dictionaries with multi-type features are learned to capture the essential structures of the image. Then, it's proposed to adaptively control the weights of the high resolution(HR) patches obtained by different dictionaries. Numerous experiments validate that this proposed framework brings better results in terms of both objective quantitation and visual perception than other compared algorithms. (C) 2015 Elsevier B.v. All rights

关键词： Super-resolution Remote sensing images Dictionary interpreting Sparse representation

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Ongoing Tests and Improvements of the MPS Algorithm for the Automatic Crack Detection Within Grey Level Pavement images

Ongoing Tests and Improvements of the MPS Algorithm for the ...

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European Signal processing Conference

作者： v. Baltazart Ph. Nicolle L. Yang Ifsttar UBL University Nantes France Xi'an Jiaotong University Shaanxi China

The MPS approach (Minimal Path Selection) has shown in [1] to provide robust and accurate segmentation of cracks within pavement images compared to other algorithms. As a counterpart, MPS suffers from a large computing time. In this paper, we present three different ongoing improvements to reduce the computing time and to improve the overall segmentation performance. Most of the work focuses on the first three steps of the algorithm which achieve the segmentation of the crack skeleton. This is at first the improvement of the MPS methodology under Matlab coding, then, the C language MPS version and finally, the first attempt to parallelize MPS under the GPU platform. The results on pavement images illustrate the achieved improvements in terms of better segmentation and faster computational time.

关键词： Graphics processing units image segmentation Skeleton MATLAB Encoding Surface cracks Real-time systems

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ProxImaL: Efficient image Optimization using Proximal algorithms

ProxImaL: Efficient Image Optimization using Proximal Algori...

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ACM SIGGRAPH Conference

作者： Heide, Felix Diamond, Steven Niessner, Matthias Ragan-Kelley, Jonathan Heidrich, Wolfgang Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA Univ British Columbia Vancouver BC V5Z 1M9 Canada KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781450342797

Computational photography systems are becoming increasingly diverse, while computational resources-for example on mobile platforms-are rapidly increasing. As diverse as these camera systems may be, slightly different variants of the underlying image processing tasks, such as demosaicking, deconvolution, denoising, inpainting, image fusion, and alignment, are shared between all of these systems. Formal optimization methods have recently been demonstrated to achieve state-of-the-art quality for many of these applications. Unfortunately, different combinations of natural image priors and optimization algorithms may be optimal for different problems, and implementing and testing each combination is currently a time-consuming and error-prone process. ProxImaL is a domain-specific language and compiler for image optimization problems that makes it easy to experiment with different problem formulations and algorithm choices. The language uses proximal operators as the fundamental building blocks of a variety of linear and nonlinear image formation models and cost functions, advanced image priors, and noise models. The compiler intelligently chooses the best way to translate a problem formulation and choice of optimization algorithm into an efficient solver implementation. In applications to the image processing pipeline, deconvolution in the presence of Poisson-distributed shot noise, and burst denoising, we show that a few lines of ProxImaL code can generate highly efficient solvers that achieve state-of-the-art results. We also show applications to the nonlinear and nonconvex problem of phase retrieval.

关键词： computational photography digital image processing optimization

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Adaptive guided image filter for warping in variational optical flow computation

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SIGNAL processing 2016年 127卷 253-265页

作者： Tu, Zhigang Poppe, Ronald veltkamp, Remco C. Arizona State Univ Sch Comp Informat Decis Syst Engn Tempe AZ 85287 USA Univ Utrecht Dept Informat & Comp Sci Utrecht Netherlands

The variational optical flow method is considered to be the standard method to calculate an accurate dense motion field between successive frames. It assumes that the energy function has spatiotemporal continuities and appearance motions are small. However, for real image sequences, the temporal continuity assumption is often violated due to outliers and occlusions, causing inaccurate flow vectors at these regions. After each warping operation, errors are generated at the corresponding regions of the warped interpolation image. This results in an inaccurate discrete approximation of the temporal derivative and thus ends up affecting the accuracy of the estimated flow field. In this paper, we propose an adaptive guided image filter to correct these errors in the warped interpolation image. A guidance image is reconstructed by considering both the feature of the reference image as well as the difference between the warped interpolation image and the reference image, to guide the filtering of the warped interpolation image. To adjust the smoothing degree, the regularization parameter in the guided image filter is adaptively selected based on a confidence measure. Extensive experiments on different datasets and comparison with state-of-the-art variational optical flow algorithms demonstrate the effectiveness of our method. (C) 2016 Elsevier B.v. All rights reserved.

关键词： variational optical flow Warped interpolation image correction Adaptive guided image filter

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Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2016年 127卷 376-394页

作者： An, Nan Palmer, Christine M. Baker, Robert L. Markelz, R. J. Cody Ta, James Covington, Michael F. Maloof, Julin N. Welch, Stephen M. Weinig, Cynthia Kansas State Univ Dept Agron Manhattan KS 66506 USA Univ Calif Davis Dept Plant Biol Davis CA 95616 USA Univ Wyoming Dept Bot Laramie WY 82071 USA

Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, cost-inefficient, and often destructive manual sampling methods that were also prone to observer error. In recent years, photogrammetry and image processing techniques have been introduced to plant phenotyping, but cost efficiency issues remain when combining these two techniques within large-scale plant phenotyping studies. Using these high throughput techniques in basic plant biology research and agriculture are still in the developmental stages but show great promise for rapid phenotyping, which will materially aid both science and crop improvement efforts. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations. (C) 2016 Elsevier B.v. All rights reserved.

关键词： High-throughput Phenotyping Photogrammetry image processing Shade avoidance Arabidopsis thaliana

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