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Adaptive fast composite splitting algorithm for MR image reconstruction 1

2nd International Conference on Machine Intelligence and signal processing, MISP 2019

作者： Mathew, Raji Susan Paul, Joseph Suresh Medical Image Computing and Signal Processing Lab Indian Institute of Information Technology and Management TrivandrumKerala India

ISBN: (数字)9789811513664

ISBN: (纸本)9789811513657

Reconstruction of an image from undersampled k-space data using conventional sparsity priors use any of the pre-specified transforms like wavelet, which utilizes the global sparsity of images. Alternatively, methods such as block-matching and 3D filtering (BM3D) utilize the non-local sparsity of images, resulting in better preservation of finer details. However, use of either of the methods cannot utilize global and non-local sparsity at the same time. In this paper, we propose an efficient combination of the global and non-local sparsity realized using wavelet and BM3D, implemented using the fast composite splitting denoising (FCSD) algorithm. Furthermore, we use an adaptive regularization parameter for the wavelet to yield the best achievable performance using adaptive thresholded Landweber (ATL). The proposed adaptive fast composite splitting denoising algorithm (AFCSD) combines the advantages of both BM3D with adaptive TL through the use of FCSD and thus exploits the self-repetitive structure in images by enforcing non-local sparsity as well as entire consistency of the image. Experimental results reveal that the proposed method is superior to any of the individual methods in terms of reconstruction accuracy and computation complexity. Additionally, when used as an auxiliary constraint in dictionary learning (DL), this can result in further improvements in reconstruction with significant reduction in the computation time as compared to a conventional DL reconstruction. © Springer Nature Singapore Pte Ltd. 2020.

关键词： image reconstruction

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Modelling of random extra pulses during quasi-closed glottal cycle phases 10

Modelling of random extra pulses during quasi-closed glottal...

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10th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2017

作者： Aichinger, P. Roesner, I. Schoentgen, J. Pernkopf, F. Department of Otorhinolaryngology Division of Phoniatrics-Logopedics Medical University of Vienna Austria F.N.R.S. Université Libre de Bruxelles Laboratories of Image Signal Processing and Acoustics Faculty of Applied Sciences Brussels Belgium Signal Processing and Speech Communication Lab Graz University of Technology Austria

ISBN: (纸本)9788864536064

The presence of random extra pulses during quasi-closed glottal cycle phases may constitute a distinct voice quality type relevant to the clinical care of disordered voices. In this paper, we propose for this voice type a glottal area waveform model that includes automatic parameter estimation. The model involves (1) extraction of the fundamental frequency, (2) estimation of the cyclic pulse times, heights and shapes, (3) Fourier synthesis of a cyclic pulse train model, (4) closed phases estimation via fitting an inverted parabola to the averaged pulse shape, (5) estimation of the random extra pulses’ positions and shapes, and (6) pulse shape filtering based synthesis of the random extra pulses. For a typical voice sample, the root mean square error energy level of the purely cyclic model = -13.2 dB, which improves by 1.5 dB when extra pulses are added to the model. © Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA *** right reserved.

关键词： Mean square error

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Rapid and multimodal in vivo bioimaging of cancer cells through in situ biosynthesis of Zn＆Fe nanoclusters

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Nano Research 2017年第8期10卷 2626-2632页

作者： Tianyu Du Chunqiu Zhao Fawad ur Rehman Lanmei Lai Xiaoqi Li Yi Sun Shouhua Luo Hui Jiang Matthias Selke Xuemei Wang State Key Laboratory of Bioelectronics (Chien-Shiung Wu Lab) School of Biological Science and Medical Engineering Southeast University Nanjing 210096 China Nanjing Foreign Language School Nanjing 210096 China Laboratory of the signal and image processing School of Biological Science and Medical Engineering Southeast University Nanjing 210096 China Department of Chemistry and Biochemistry California State University Los Angeles CA 90032 USA

Early diagnosis remains highly important for efficient cancer treatment, and hence, there is significant interest in the development of effective imaging strategies. This work reports a new multimodal bioimaging method for accurate and rapid diagnosis of cancer cells by introducing aqueous Fe^2＋ and Zn^2＋ ions into cancer cells （i.e., HeLa, U87, and HepG2 cancer cells）. We found that the biocompatible metal ions Fe^2＋ and Zn^2＋ forced the cancer cells to spontaneously synthesize fluorescent ZnO nanoclusters and magnetic Fe3O4 nanoclusters. These clusters could then be used for multimodal cancer imaging by combining fluorescence imaging with magnetic resonance imaging and computed tomography imaging. Meanwhile, for normal cells （i.e., L02） and tissues, neither fluorescence nor any other obvious difference could be detected between pre- and post-injection. This multimodal bioimaging strategy based on the in situ biosynthesized Zn＆Fe oxide nanoclusters might therefore be useful for early cancer diagnosis and therapy.

关键词： multimodal imaging biosynthesized Zn&Fe oxide nanoclusters cancer diagnosis

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Combination of global and nonlocal sparse regularization priors for MR image reconstruction

Combination of global and nonlocal sparse regularization pri...

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IEEE Region 10 International Conference TENCON

作者： Raji Susan Mathew Joseph Suresh Paul Medical Image Computing and Signal Processing Lab Indian Institute of Information Technology and Management Kerala

Magnetic resonance (MR) image recovery from the undersampled k-space data using fixed basis transforms such as wavelet utilizes the global sparsity of images. On the other hand, redundant representation schemes such as block-matching and 3D filtering (BM3D) make use of the the non-local sparsity of images, resulting in better preservation of finer details. But, use of any of these individual methods cannot utilize global and non-local sparsity at the same time. In this paper, we propose an algorithm for efficient combination of the global and non-local sparsity through the use of the fast composite splitting denoising (FCSD) algorithm. Here wavelets and BM3D are used for global and non-local sparsity, respectively. Additionally, we use adaptive iterative soft thresholding algorithm (ISTA) for wavelet recovery that uses an adaptive regularization parameter (threshold) in each iteration. The proposed adaptive fast composite splitting denoising (AFCSD) algorithm combines the merits of both BM3D with adaptive ISTA. This is because, it utilizes the self-repetitive structure in images by enforcing nonlocal sparsity as well as entire consistency of the image. The proposed method is superior to any of the individual methods in terms of reconstructed image quality, computation complexity and compute time. Furthermore, we show that the combined sparsity constraint can yield further improvements in reconstruction with significant reduction in the compute time when used as an auxiliary constraint in Dictionary Learning (DL), compared to a standard DL reconstruction.

关键词： image reconstruction Wavelet transforms signal processing algorithms Convergence Three-dimensional displays Noise reduction

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CANONICAL CEREBELLAR GRAPH WAVELETS AND THEIR APPLICATION TO FMRI ACTIVATION MAPPING

CANONICAL CEREBELLAR GRAPH WAVELETS AND THEIR APPLICATION TO...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Hamid Behjat Nora Leonardi Leif Sornmo Dimitri Van De Ville Signal Processing Group Department of Biomedical Engineering Lund University Medical Image Processing Lab (MIPLAB) Ecole Polytechnique Federale de Lausanne

ISBN: (纸本)9781424479276

Wavelet-based statistical parametric mapping (WSPM) is an extension of the classical approach in fMRI activation mapping that combines wavelet processing with voxel-wise statistical testing. We recently showed how WSPM, using graph wavelets tailored to the full gray-matter (GM) structure of each individual's brain, can improve brain activity detection compared to using the classical wavelets that are only suited for the Euclidian grid. However, in order to perform analysis on a subject-invariant graph, canonical graph wavelets should be designed in normalized brain space. We here introduce an approach to define a fixed template graph of the cerebellum, an essential component of the brain, using the SUIT cerebellar template. We construct a corresponding set of canonical cerebellar graph wavelets, and adopt them in the analysis of both synthetic and real data. Compared to classical SPM, WSPM using cerebellar graph wavelets shows superior type-I error control, an empirical higher sensitivity on real data, as well as the potential to capture subtle patterns of cerebellar activity.

关键词： Statistical testing Functional MRI Cerebellum Spectral graph theory Graph wavelet transform Wavelet thresholding

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Assessment of depth of anesthesia using principal component analysis

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Journal of Biomedical Science and Engineering 2009年第1期2卷 9-15页

作者： Mina Taheri Behzad Ahmadi Rassoul Amirfattahi Mojtaba Mansouri Digital Signal Processing Research Lab Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156-83111 Iran Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran. Correspondence should be addressed to Mina Taheri

A new approach to estimating level of uncon-sciousness based on Principal Component Analysis (PCA) is proposed. The Electroen-cephalogram (EEG) data was captured in both Intensive Care Unit (ICU) and operating room, using different anesthetic drugs. Assuming the central nervous system as a 20-tuple source, window length of 20 seconds is applied to EEG. The mentioned window is considered as 20 nonoverlapping mixed-signals (epoch). PCA algorithm is applied to these epochs, and larg-est remaining eigenvalue (LRE) and smallest remaining eigenvalue (SRE) were extracted. Correlation between extracted parameters (LRE and SRE) and depth of anesthesia (DOA) was measured using Prediction probability (PK). The results show the superiority of SRE than LRE in predicting DOA in the case of ICU and isoflurane, and the slight superiority of LRE than SRE in propofol induction. Finally, a mixture model containing both LRE and SRE could predict DOA as well as Relative Beta Ratio (RBR), which expresses the high capability of the proposed PCA based method in estimating DOA.

关键词： Bispectral Index Depth of Anesthesia Eignevalue Decomposition Principal Component Analysis

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STATISTICAL PARAMETRIC MAPPING OF FUNCTIONAL MRI DATA USING WAVELETS ADAPTED TO THE CEREBRAL CORTEX

STATISTICAL PARAMETRIC MAPPING OF FUNCTIONAL MRI DATA USING ...

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IEEE International Symposium on Biomedical Imaging

作者： Hamid Behjat Nora Leonardi Dimitri Van De Ville Signal Processing Group Department of Electrical and Information Technology Lund University Medical Image Processing Lab (MIPLAB) Ecole Polytechnique Federale de Lausanne

ISBN: (纸本)9781467364560

Wavelet approaches have been successfully applied to the detection of brain activity in fMRI data. Spatial activation patterns have a compact representation in the wavelet domain. However, classical wavelets designed for regular Euclidean spaces are not optimal for the topologically complicated gray-matter (GM) domain where activation is expected. We hypothesized that wavelet bases that are adapted to the structure of the GM, would be more powerful in detecting brain activity. We therefore combine (1) a GM-based graph wavelet transform as an advanced spatial transformation for fMRI data with (2) the wavelet-based statistical parametric mapping framework (WSPM). We introduce suitable design choices for the graph wavelet transform and evaluate the performance of the proposed approach both on simulated and real fMRI data. Compared to SPM and conventional WSPM, the graph-based WSPM shows improved detection of finely 3D-structured brain activity.

关键词： Statistical testing Functional MRI Spectral graph theory Graph wavelet transform Wavelet thresholding

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A Design of Software and Hardware System for Pre-hospital Emergency Treatment

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Chinese Journal of Biomedical Engineering(English Edition) 2020年第3期29卷 1-9页

作者： Bingxu Ye Feng Chen Shaomin Ou Yang Xie Yaowen Chen The Key Lab of Digital Signal and Image Processing of Guangdong Shantou Guangdong 515063China Shantou University Medical College Shantou Guangdong 515041China The Second Affiliated Hospital of Shantou University Medical College Shantou Guangdong 515041China

This paper aims to improve the pre-hospital first-aid knowledge and pre-hospital first-aid success rate and optimize the pre-hospital emergency *** paper mainly discusses a comprehensive pre-hospital emergency system,which consists of the hardware part and software *** hardware is based on the international cardiopulmonary resuscitation guidelines,and a cardiopulmonary resuscitation(CPR)training auxiliary equipment is developed,which can be applied in pre-hospital first aid training to help people perform accurate chest *** software part is based on the WeChat applet platform to set up a pre-hospital first-aid dispatching system that connects callers and CPR *** software system effectively improves the pre-hospital CPR first-aid system and increases the rescue success rate of cardiac arrest patients.

关键词： Chest compression CPR Auxiliary equipment WeChat applet

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Investigating the stability of fast iterative shrinkage thresholding algorithm for MR imaging reconstruction using compressed sensing

Investigating the stability of fast iterative shrinkage thre...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Guishan Zhang Haitao Deng Yaowen Chen Zhiwei Shen Renhua Wu Key Lab. of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou China Department of Medical Imaging 2nd Affiliated Hospital Medical College of Shantou University Shantou China

ISBN: (纸本)9781467376839

Compressed sensing (CS) has been applied to the field of sub-sampled magnetic resonance imaging (MRI) reconstruction (CS-MRI). Fast iterative shrinkage-thresholding algorithm (FISTA) is an effective method for CS-MR images reconstruction. To investigate the accuracy and efficiency of the proposed algorithm, we applied it to the under-sampling MR images gained by different MRI scanning sequences. We found the peak signal to noise ratio (PSNR) of reconstructed MRI with varying sampling ratios diminished from Axial T1 weighted images (Ax T1) (45.13±12.97 dB), Axial T2 weighted images (Ax T2) (42.8 ± 14.95 dB), FLAIR (41.74 ± 14.15 dB), Diffusion Weighted Imaging (DWI) (40.23 ±17.40 dB) and Sagittal T2 weighted images (Sag T2) (36.28±12.32 dB), but there was no significant difference among the groups. In addition, the changes to the elapsed time of them was minor, Ax T1 (1.09±0.13 s), Ax T2 (1.30±0.13s), FLAIR (1.02±0.12s), DWI (1.07±0.13s) and Sag T2 (1.12±0.07s). Our results confirmed the stability of the proposed fast MRI reconstruction method for different scanning sequences. Further efforts are still needed to design the clinical sequence with sub-sampled acquisition strategy which may be a developing technique with clinical value.

关键词： Compressed Sensing Magnetic Resonance Imaging PSNR Fast Iterative Shrinkage-thresholding Algorithm (FISTA)

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PDA-based system with teleradiology and image analysis capabilities

PDA-based system with teleradiology and image analysis capab...

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29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), vol.8

作者： Pantelis Georgiadis Dionisis Cavouras Antonis Daskalakis Koralia Sifaki Menelaos Malamas George Nikiforidis Ekaterini Solomon Medical Image Processing and Analysis Group (M.I.P.A.) Laboratory of Medical Physics School of Medicine University of Patras Greece Medical Signal and Image Processing Lab Department of Medical Instruments Technology Technological Education Institute of Athens Athens Greece MRI Unit General Hellenic Airforce Hospital Athens Greece Department of Radiology School of Medicine University of Patras Rio-Patras Greece

The aim of the present study was to design and implement a Personal Digital Assistant (PDA)-based teleradiology system incorporating image processing and analysis facilities for use in emergency situations within a hospital environment The system comprised a DICOM-server, connected to an MRI unit, 3 wireless access points, and 3 PDAs (HP iPaq rx3715). PDA application software was developed in MS Embedded Visual C++ 4.0. Each PDA can receive, load, process and analyze hi-quality static MR images. image processing includes gray-scale manipulation and spatial filtering techniques while image analysis incorporates a probabilistic neural network (PNN) classifier, which was optimally designed employing a suitable combination of textural features and was evaluated using the leave-one-out method. The PNN is capable of discriminating between three major types of human brain tumors with accuracy of 86.66%. The developed application may be useful as a mobile medical tele-consultation tool.

关键词： image analysis Personal digital assistants image processing Hospitals Magnetic resonance imaging Application software Gray-scale Filtering Biological neural networks Humans

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