检索结果-内蒙古大学图书馆

Fast signal recovery from saturated measurements by linear loss and nonconvex penalties

学校读者我要写书评

暂无评论

arXiv 2018年

作者： He, Fan Huang, Xiaolin Liu, Yipeng Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The MOE Key Laboratory of System Control and Information Processing Shanghai200240 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China The Department of Computational Mathematics Science and Engineering Michigan State University MI United States

Sign information is the key to overcoming the inevitable saturation error in compressive sensing systems, which causes information loss and results in bias. For sparse signal recovery from saturation, we propose to use a linear loss to improve the effectiveness from existing methods that utilize hard constraints/hinge loss for sign consistency. Due to the use of linear loss, an analytical solution in the update progress is obtained, and some nonconvex penalties are applicable, e.g., the minimax concave penalty, the 0 norm, and the sorted 1 norm. Theoretical analysis reveals that the estimation error can still be bounded. Generally, with linear loss and nonconvex penalties, the recovery performance is significantly improved, and the computational time is largely saved, which is verified by the numerical experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Compressed sensing

A reduced-reference image quality assessment model based on joint-distribution of neighboring LOG signals 12

学校读者我要写书评

暂无评论

A reduced-reference image quality assessment model based on ...

Digital Photography and Mobile Imaging XII 2016

作者： Chen, Congmin Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University China

Previous work have validated that the output of retinal ganglion cells in human visual pathway, which can be modeled as an LOG (Laplacian of Gaussian) filtration, can whiten the power spectrum of not only the natural images, but also the distorted images, hence the first-order (average luminance) and the secondorder (contrast) redundancies have been removed when applying the LOG filtration. Considering the fact that human vision system (HVS) always ignores the first-order and the second-order information when sensing image local structures, the LOG signals should be efficient features in IQA (image quality assessment) task and a lot of LOG based IQA models have been proposed. In this paper, we focus on an interesting question that has not been investigated carefully yet: what is an efficient way to represent image structure features that is perceptual quality aware based on the LOG signals. We examine the relationship between neighboring LOG signals and propose to represent the relationship by computing the joint distribution of neighboring LOG signals, and thus propose a set of simple but efficient RR IQA feature and consequently yield an excellent RR IQA model. Experimental results on three large scale subjective IQA databases show that our proposed method works robustly across different databases and stay in the state-of-the-art RR IQA models. © 2016 Society for Imaging Science and Technology.

关键词： Laplace transforms

MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging - design, implementation and application on the example of DCE-MRI

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Debus, Charlotte Floca, Ralf Ingrisch, Michael Kompan, Ina Maier-Hein, Klaus Abdollahi, Amir Nolden, Marco Heidelberg Germany Heidelberg Germany Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Heidelberg Institute of Radiation Oncology [HIRO Division of Medical Image Computing German Cancer Research Center Dkfz Department of Radiology University Hospital Munich Ludwig-Maximilians-University Munich Section Pattern Recognition Department of Radiation Oncology Heidelberg University Hospital Heidelberg

Background: Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)/computed tomography (CT), apparent diffusion coefficient calculations and intravoxel incoherent motion modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-Alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow. Results: We present a framework for medical image fitting tasks that is included in the Medical Imaging Interaction Toolkit MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-To-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre-and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth. Conclusions: Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform

关键词： Magnetic resonance imaging

A mammography-based classification method for breast masses using SVM

学校读者我要写书评

暂无评论

Boletin Tecnico/Technical Bulletin

Boletin Tecnico/Technical Bulletin 2017年第12期55卷 1-9页

作者： Liang, Jingjing Lan, Yihua Li, Zhengyan Bai, Kun Zhang, Xingang School of Computer and Information Technology Nanyang Normal University Nanyang473061 China Institute of Image Processing and Pattern Recognition Nanyang Normal University Nanyang473061 China

A significant step in the computer-aided diagnosis of mammary cancer is identifying the suspected masses. The sample data is used for training the classifier before the cases to be measured are being identified. Therefore, the design of classifier greatly affects the overall performance of the system. Since the number of samples is commonly limited, we extract high-precision feature values to better identify the suspected masses. A nonlinear classifier SVM is introduced into this system, and it is compared with Fisher in classifying effects by carrying out a series of experiments. The Receiver Operating Characteristic Curve (ROC) shows that Az value has reached up to *** experiment results prove that the application of this classifier in identifying and classifying suspected breast lumps helps improve the overall performance of the computer-aided diagnosis system.

关键词： Computer aided diagnosis

SAR and optical image registration based on edge features

学校读者我要写书评

暂无评论

SAR and optical image registration based on edge features

International Conference on Systems and Informatics (ICSAI)

作者： Donghao Shen Junhao Zhang Jie Yang Deying Feng Jiang Li Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Liaocheng University China Shanghai Xinyue Instrument Factory China

As synthetic aperture radar (SAR) data is more and more popular in recent days, the fusion of SAR and optical image become inevitable and desirable problem. image Registration is the fundamental for data fusion, and has been wildly studied. However, literature on registration between SAR and optical image remain little, which means the problem is still challenge and open. On one hand, the SAR and optical image are from totally different sensor with variant imaging mechanism. On the other hand, Simple features could not enough to maintain the accuracy and efficiency of multi-modal registration. We propose a new method for SAR and optical image registration. Different from current methods mainly based on intensity feature or point feature, our method matches two images by their boundary features. A globalized boundary detection algorithm are used for feature extraction. The extracted boundaries were then matched by coherence point drift algorithm. Experiments prove that our method could achieve pixel-level precision, and perform robust for different resolutions.

关键词： Optical imaging Synthetic aperture radar Optical sensors Adaptive optics image registration image edge detection Feature extraction

A novel color image encryption algorithm based on genetic recombination and the four-dimensional memristive hyperchaotic system

学校读者我要写书评

暂无评论

Chinese Physics B 2016年第10期25卷 76-88页

作者：柴秀丽甘志华路杨张苗辉陈怡然 School of Computer and Information Engineering Institute of Image Processing and Pattern Recognition Henan University Department of Electrical and Computer Engineering University of Pittsburgh School of Software Henan University Research Department Henan University

Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.

关键词： genetic recombination memristive hyperchaotic system color image encryption confusion diffusion

3D RoI-aware U-net for accurate and efficient colorectal tumor segmentation

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Huang, Yi-Jie Dou, Qi Wang, Zi-Xian Liu, Li-Zhi Jin, Ying Li, Chao-Feng Wang, Lisheng Chen, Hao Xu, Rui-Hua Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Imsight Medical Technology Co. Ltd. China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China

Segmentation of colorectal cancerous regions from 3D Magnetic Resonance (MR) images is a crucial procedure for radiotherapy which conventionally requires accurate delineation of tumour boundaries at an expense of labor, time and reproducibility. While deep learning based methods serve good baselines in 3D image segmentation tasks, small applicable patch size limits effective receptive field and degrades segmentation performance. In addition, Regions of interest (RoIs) localization from large whole volume 3D images serves as a preceding operation that brings about multiple benefits in terms of speed, target completeness, reduction of false positives. Distinct from sliding window or non-joint localization-segmentation based models, we propose a novel multitask framework referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI localization and in-region segmentation where the two tasks share one backbone encoder network. With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detail-preserving segmentation and therefore enlarged applicable volume size and effective receptive field. To effectively train the model, we designed a Dice formulated loss function for the global-to-local multi-task learning procedure. Based on the efficiency gains demonstrated by the proposed method, we went on to ensemble models with different receptive fields to achieve even higher performance costing minor extra computational expensiveness. Extensive experiments were subsequently conducted on 64 cancerous cases with a four-fold cross-validation, and the results showed significant superiority in terms of accuracy and efficiency over conventional state-of-the art frameworks. In conclusion, the proposed method has a huge potential for extension to other 3D object segmentation tasks from medical images due to its inherent generalizability. The code for the proposed method is publicly available. Copyright ©

关键词： Signal encoding

Dictionary learning based statistical interior reconstruction without a prior knowledge 10

学校读者我要写书评

暂无评论

Dictionary learning based statistical interior reconstructio...

Developments in X-Ray Tomography X

作者： Shi, Yongyi Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an Shaanxi710049 China

ISBN: (纸本)9781510603257

Despite the significantly practical utilities of interior tomography, it still suffers from severe degradation of direct current (DC) shift artifact. Existing literature suggest to introducing prior information of object support (OS) constraint or the zeroth order image moment, i.e., the DC value into interior reconstruction to suppress the shift artifact, while the prior information is not always available in practice. Aimed at alleviating the artifacts without prior knowledge, in this paper, we reported an approach on the estimation of the object support which could be employed to estimate the zeroth order image moment, and hence facilitate the DC shift artifacts removal in interior reconstruction. Firstly, by assuming most of the reconstructed object consists of soft tissues that are equivalent to water, we reconstructed a virtual OS that is symmetrical about the interior region of interest (ROI) for the DC estimation. Hence the DC value can be estimated from the virtual reconstruction. Secondly, a statistical iterative reconstruction incorporated with the sparse representation in terms of learned dictionary and the constraint in terms of image DC value was adopted to solve the interior tomography. Experimental results demonstrate that the relative errors of the estimated zeroth order image moment are 4.7% and 7.6%, corresponding to the simulated data of a human thorax and the real data of a sheep lung, respectively. Reconstructed images with the constraint of the estimated DC value exhibit greatly superior image quality to that without DC value constraint. © Copyright 2016 SPIE.

关键词： image segmentation

Scale-space anisotropic total variation for limited angle tomography

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Huang, Yixing Taubmann, Oliver Huang, Xiaolin Haase, Viktor Lauritsch, Guenter Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nuremberg Erlangen Germany Pattern Recognition Lab FriedrichAlexander-University Erlangen-Nuremberg Erlangen Germany Erlangen Germany Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Siemens Healthcare GmbH Forchheim Germany Department of Radiology University of Utah Salt Lake CityUT United States

This paper addresses streak reduction in limited angle tomography. Although the iterative reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept at eliminating large ones since total variation (TV) regularization is scale-dependent and may regard these streaks as homogeneous areas. Hence, the main purpose of this paper is to reduce streak artifacts at various scales. We propose the scale-space anisotropic total variation (ssaTV) algorithm in two different implementations. The first implementation (ssaTV-1) utilizes an anisotropic gradient-like operator which uses 2 · s neighboring pixels along the streaks' normal direction at each scale s. The second implementation (ssaTV-2) makes use of anisotropic down-sampling and up-sampling operations, similarly oriented along the streaks' normal direction, to apply TV regularization at various scales. Experiments on numerical and clinical data demonstrate that both ssaTV algorithms reduce streak artifacts more effectively and efficiently than wTV, particularly when using multiple scales. Copyright © 2017, The Authors. All rights reserved.

关键词： Anisotropy