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DYNAMIC 3D PET RECONSTRUCTION FOR KINETIC ANALYSIS USING PATCH-BASED LOW-RANK PENALTY

DYNAMIC 3D PET RECONSTRUCTION FOR KINETIC ANALYSIS USING PAT...

引用

IEEE Nuclear Science Symposium and Medical Imaging Conference

作者： K. S. Kim Y. D. Son Z. H. Cho J. B. Ra J. C. Ye Bio Imaging & Signal Processing Lab. Dept. of Bio& Brain Engineering KAIST Neuroscience Research Institute Gachon University of Medicine and Science Image Systems Lab. Electrical Engineering KAIST

ISBN: (纸本)9781467320283

Dynamic positron emission tomography (PET) is widely used to identify metabolism over time. However, conventional reconstruction algorithm provides a noisy reconstruction due to the lack of photon counts in each frame. Therefore, the main goal of this paper is to develop a novel spatio-temporal regularization approach that exploits inherent similarities within intra- and inter- frames. One of the main contributions of this paper is to demonstrate that such correlations can be exploited using a low rank constraint of overlapping similarity blocks. The resulting optimization framework is, however, non-smooth and non Lipschitz due to the low-rank penalty terms and Poisson log-likelihood. Therefore, we propose a novel globally convergent optimization method using the concave-convex procedure (CCCP) by exploiting Legendre-Fenchel *** confirm that the proposed algorithm can provide significantly improved image quality.

关键词： CCCP PET LOW-RANK PENALTY

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A novel segmentation framework for uveal melanoma in magnetic resonance imaging based on class activation maps 2

A novel segmentation framework for uveal melanoma in magneti...

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2nd International Conference on Medical Imaging with Deep Learning, MIDL 2019

作者： Nguyen, Huu-Giao Pica, Alessia Hrbacek, Jan Weber, Damien C. La Rosa, Francesco Schalenbourg, Ann Sznitman, Raphael Cuadra, Meritxell Bach Proton Therapy Center Paul Scherrer Institut ETH Domain Villigen Switzerland Ophthalmic Technology Lab. ARTORG Center University of Bern Switzerland Switzerland Radiation Oncology Department Inselspital University of Bern Switzerland Signal Processing Lab. Ecole Polytechnique Fédérale de Lausanne Switzerland Adult Ocular Oncology Unit Jules-Gonin Eye Hospital Lausanne Switzerland Medical Image Analysis Laboratory CIBM University of Lausanne Switzerland

An automatic and accurate eye tumor segmentation from Magnetic Resonance images (MRI) could have a great clinical contribution for the purpose of diagnosis and treatment planning of intra-ocula cancer. For instance, the characterization of uveal melanoma (UM) tumors would allow the integration of 3D information for the radiotherapy and would also support further radiomics studies. In this work, we tackle two major challenges of UM segmentation: 1) the high heterogeneity of tumor characterization in respect to location, size and appearance and, 2) the difficulty in obtaining ground-truth delineations of medical experts for training. We propose a thorough segmentation pipeline consisting of a combination of two Convolutional Neural Networks (CNN). First, we consider the class activation maps (CAM) output from a Resnet classification model and the combination of Dense Conditional Random Field (CRF) with a prior information of sclera and lens from an Active Shape Model (ASM) to automatically extract the tumor location for all MRIs. Then, these immediate results will be inputted into a 2D-Unet CNN whereby using four encoder and decoder layers to produce the tumor segmentation. A clinical data set of 1.5T T1-w and T2-w images of 28 healthy eyes and 24 UM patients is used for validation. We show experimentally in two different MRI sequences that our weakly 2D-Unet approach outperforms previous state-of-the-art methods for tumor segmentation and that it achieves equivalent accuracy as when manual lab.ls are used for training. These results are promising for further large-scale analysis and for introducing 3D ocular tumor information in the therapy planning. © 2019 H.-G. Nguyen, A. Pica, J. Hrbacek, D. Weber, F.L. Rosa, A. Schalenbourg, R. Sznitman & M. Bach Cuadra.

关键词： Magnetic resonance imaging

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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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image normalization for quantitative immunohistochemistry in digital pathology

Image normalization for quantitative immunohistochemistry in...

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IEEE International Symposium on Biomedical Imaging

作者： Yves-Rémi Van Eycke Justine Allard Mélanie Derock Isabelle Salmon Olivier Debeir Christine Decaestecker Lab. of Image Signal processing & Acoustics Université Libre de Bruxelles (ULB) Brussels Belgium DIAPath Center for Microscopy and Molecular Imaging (ULB) Gosselies Belgium Department of Pathology Université Libre de Bruxelles (ULB) Brussels Belgium

ISBN: (纸本)9781479923519

We propose to adapt to immunohistochemistry (IHC) some methods proposed to normalize images from histological slices stained with hematoxylin-eosin (H&E). Our final aim is to provide a coherent quantitative characterization of IHC biomarkers across different IHC batches with possible staining variations. In contrast to H&E, IHC staining strongly varies with the tissue analyzed and the protein targeted, making image normalization challenging. To solve this problem, we added in each IHC batch a slice from a reference tissue microarray (TMA) and then digitalized it to establish an inter-batch normalization transform. A comparison of two methods adapted to the specificity of IHC-stained slides evidences some normalization requirements to make valid IHC biomarker quantification across different staining batches.

关键词： image color analysis Biological tissues Proteins Deconvolution Biomarkers Immune system Pathology

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Supervised classification of scatterers on sar imaging based on incoherent polarimetric time-frequency signatures

Supervised classification of scatterers on sar imaging based...

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17th European signal processing Conference, EUSIPCO 2009

作者： Duquenoy, M. Ovarlez, J.P. Ferro-Famil, L. Pottier, E. ONERA French Aerospace Lab. DEMR / Signal Processing Unit Chemin de la Hunière F-91761 Palaiseau Cedex France IETR Image and Remote Sensing Group SAPHIR Team University of Rennes 1 263 avenue du Général Leclerc CS-74205 Rennes Cedex France SONDRA Supelec Plateau du Moulon 3 rue Joliot-Curie F-91192 Gif-sur-Yvette Cedex France

This paper deals with the analysis of the non-stationary behavior of scatterers in polarimetric SAR imaging. A method based on continuous wavelet and incoherent polarimetric decompositions is proposed to extract the polarimetric time-frequency signatures of scatterers. These signatures characterize scatterers according to their polarimetric/or energetic behavior versus the emitted frequency and the observation angle. Then, signatures from reference targets are used to train a multi-layer perceptron (MLP). All in all, SAR imaging data are classified by the MLP. The efficiency of this method is demonstrated, for the deterministic targets (manmade targets). It can be explained by the fact that the manmade targets present a strong non-stationary behavior. But for the vegetation and canopy the results are not convincing. It can be interpreted by the fact that the behavior of vegetation is stationary. © EURASIP, 2009.

关键词： Vegetation

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Binary pattern flavored feature extractors for Facial Expression Recognition: An overview

Binary pattern flavored feature extractors for Facial Expres...

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Proceedings of the International Convention MIPRO

作者： Rasmus Lyngby Kristensen Zheng-Hua Tan Zhanyu Ma Jun Guo Section of Image Analysis and Computer Graphics Technical University of Denmark Kgs. Lyngby Denmark Signal and Information Processing section (SIP) Aalborg University Aalborg Denmark Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781479981748

This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression Recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER terminology, describes open challenges, and provides recommendations to scientific evaluation of FER systems. Lastly, it studies the facial expression recognition accuracy and blur invariance of the Local Frequency Descriptor. The paper seeks to bring together disjointed studies, and the main contribution is to provide a solid overview for future research.

关键词： Feature extraction Face Face recognition Databases Three-dimensional displays Accuracy Gold

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Automated Characterization of cellular migration phenomena

Automated Characterization of cellular migration phenomena

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2005 IEEE Computational Systems Bioinformatics Conference, Workshops and Poster Abstracts

作者： Beaudry, Christian Berens, Michael E. Doker, Tarek El Joy, Anna M. Karam, Lina J. Lacroix, Zoe Lutfi, Jad A. Motoru, Sai Renaut, Rosemary A. Rich, Ian J. Translational Genomics Research Institute Phoenix AZ United States Department of Electrical Engineering Machine Perception Laboratory Northern Arizona University United States Department of Electrical Engineering Image Video and Usability and Multi-D Signal Processing Labs. Arizona State University United States Department of Mechanical and Aerospace Engineering Scientific Data Management Lab. Arizona State University United States Department of Mathematics Computational Biosciences Program Arizona State University United States

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Electromagnetic brain imaging using BrainStorm

Electromagnetic brain imaging using BrainStorm

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IEEE International Symposium on Biomedical Imaging

作者： S. Baillet J.C. Masher R.M. Leahy Cognitive Neuroscience & Brain Imaging Laboratory CNRS Hôpital de la Salpêtriére Paris France CNRS Cognitive Neuroscience & Brain Imaging Lab. Paris France Signal & Image Processing Institute University of Southern California Los Angeles USA

Electromagnetic brain imaging consists of the mapping of neural generators of magnetic fields and electric potentials measured outside the head using magnetoencephalography (MEG) and electroencephalography (EEC), respectively. Here we report on a collab.rative project dedicated to the development and distribution of BrainStorm, a software suite for MEG-EEG data modeling and visualization, with integration of MRI information. BrainStorm is developed in Matlab. which ensures OS portability and facilitated interoperability with many other research software resources. Distribution of BrainStorm is managed under GNU licensing, with no charge to users.

关键词： Brain Magnetic field measurement Collab.rative software Electromagnetic fields Electric potential Electric variables measurement Electromagnetic measurements Magnetic heads Magnetoencephalography Electroencephalography

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Robust Multimodal Biometric Identification System Using Finger-Knuckle-Print features

Robust Multimodal Biometric Identification System Using Fing...

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International Conference on Control, Engineering & Information Technology

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Univ Ouargla Fac. des nouvelles technologies de l'information et de la communication Lab. de Genie Electrique Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB School of Computing Engineering and Information Sciences Northumbria University

ISBN: (纸本)9781479982134

Automatic personal identification is becoming an increasingly important requirement in a variety of applications like access control, surveillance systems and physical buildings. In recent years, biometric systems are used in these fields, offering greater convenience and several advantages over traditional security. In this paper, we propose an efficient online personal identification system based on Finger-Knuckle-Print (FKP) images. In this study, the finger image is characterized by the two dimensional Bloc based Discrete Cosine Transform (2D-BDCT) coefficients. Then, we use the Hidden Markov Model (HMM) for modeling the observation vector. In addition, the same finger is transformed by the two dimensional Discrete Fourier Transform (2D-DFT). The response of this transformation is directly used to create another observation vector. Subsequently, the two sub-systems are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. The results on a medium-size database, 150 users, show good identification performance based on individual modalities as well as after fusing multiple finger types.

关键词： Biometrics Identification FKP 2D-BDCT 2D-DFT HMM Data fusion

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Robust human identity identification system by using hand biometric traits 26

Robust human identity identification system by using hand bi...

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2014 26th International Conference on Microelectronics, ICM 2014

作者： Meraoumia, Abdallah Chitroub, Salim Bouridane, Ahmed Univ Ouargla Fac. des Nouvelles Technologies de L'information et de la Communication Lab. de Génie Électrique Ouargla30 000 Algeria Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB P.O. box 32 El Alia Bab Ezzouar Algiers16111 Algeria Department of Computer Science and Digital Technologies Northumbria University Newcastle Pandon Building Newcastle-upon-TyneNE2 1XE United Kingdom

ISBN: (纸本)9781479981533

Automatic personal identification has become an important issue in several applications, such as physical buildings and information systems. Nowadays, biometric techniques are an important and effective solution for automatic personal identification. One of the most popular biometric systems is based on the hand due to its ease of use. Hand has several modalities to be extracted, among them, Finger-Knuckle-Print (FKP) and PaLMprint (PLM), has attracted an increasing amount of attention. This paper investigates these modalities for elab.rating an efficient multimodal biometric identification system. For that, the texture information of FKP and PLM is characterized by the Local Binary Pattern (LBP). During the matching phase, an Euclidian distance score is employed to measure the similarity between templates. The proposed system is tested and evaluated using a database of 165 users. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate. © 2014 IEEE.

关键词： Biometrics

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