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

作者： Soroosh Golbabaei Amin Dadashi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Radiology Medical Image Analysis Lab. Detroit Michigan USA

Neural degeneration in Alzheimer's disease (AD) leads to structural topology deformation that in turn changes brain functionality. The main aim of the present study is to find the brain's functional connectivity network (FCN) correlates of Alzheimer's psychological test scores. To this end, the brain's FCN is extracted from the resting state functional magnetic resonance images (rs-fMRI) of healthy controls and patients with AD and represented as a graph. Then, network measures are calculated from the graphs. The correlations between the brain network measures and five AD psychological assessment test scores are evaluated. The results show positive correlation between the Mini-Mental State Examination (MMSE) and nodal strength in the left insula and negative correlation between the Clinical Dementia Rating (CDR) score and local efficiency of left olfactory cortex and also eigenvector centrality of left supramarginal cortex. The Neuropsychiatric Inventory Questionnaire (NPI-Q) also seems to be correlated with network measures of the left superior parietal gyrus. Moreover, notable decreased continuity are spotted in the limbic system. These measures can be used to provide an objective tool for AD diagnosis.

关键词： Correlation Alzheimer's disease Psychology Olfactory Brain Atmospheric measurements

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A Web-Based Tool for Semi-Automated Segmentation of Histopathological images Using Nonlinear Color Classifiers

A Web-Based Tool for Semi-Automated Segmentation of Histopat...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Marcelo Dornbusch Lopes Antonio C. Sobieranski Aldo Von Wangenheim Eros Comunello INCoD - National Brazilian Institute for Digital Convergence / LAPIX - Image Processing and Computer Graphics Lab - Federal University of Santa Catarina Brazil Postgraduate Program in Applied Computing University of Itajai Valley - UNIVALI INCoD (Nat. Brazilian Inst. for Digital Convergence) LAPIX (Image Process. & Comput. Graphics Lab.) Universidade Federal de Santa Catarina Florianopolis SC BR

ISBN: (纸本)9781467390378

Histopathological staining is a technique widely used to highlight biological states or pharmacological activities in uman tissue. A quantitative analysis of the resulting images can produce biomarkers for diseases or even other specific conditions, thus providing valuable information for diagnosis and prognosis. Since biomarkers require measurements to be made in an objective and consistent way, software systems are employed to provide this quantitative analysis. For measurements to be reproducible, the same methods must be availab.e across different lab.ratories. In this paper, we present a tool that allows users to perform quantitative analyses over the web, thus providing an efficient environment not only for individual cases to be evaluated, but also for users to share a common ground when making measurements. The classification method used by the tool to segment stained pixels is performed by a similarity function based on the polynomial version of the Mahalanobis distance, which is nonlinear and provides very robust classification for m-dimensional feature spaces. Furthermore, the similarity function can be generalized in the tool, so that images can be classified by reusing parameters of previous cases. The results of our web-based approach were compared with established ground-truth data sets, producing sensitivity, specificity and fitness values of 97.09%, 98.70%, and 97.90%.

关键词： image color analysis image segmentation Biomarkers Sensitivity Inspection Servers Statistical analysis

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Discovering true association between multimodal data sets using structured and sparse canonical correlation analysis: A simulation study

Discovering true association between multimodal data sets us...

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IEEE International Symposium on Biomedical Imaging

作者： Ali-Reza Mohammadi-Nejad Gholam-Ali Hossein-Zadeh Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Henry Ford Health System Medical Image Analysis Lab. Detroit Michigan USA

ISBN: (纸本)9781479923519

Recently, multi-modal data fusion emerged as a comprehensive approach in the field of neuroimaging. As a multivariate approach, canonical correlation analysis (CCA) is frequently used for data fusion. However, the current CCA-based fusion approaches face many problems in the sense of high-dimensionality, multi-colinearity, unimodal feature selection, asymmetric and reshaping of the imaging data into vectors. In this paper, we propose a structured and sparse CCA (ssCCA) technique to overcome the above problems. To investigate the performance of the proposed approach, we compared three multivariate and multi-modal fusion techniques: standard CCA, regularized sparse CCA, and ssCCA, with respect to their ability to detect multi-modal data associations. The results demonstrate that the ssCCA approach has superior recovery performance in the extraction of the true correlated variables relative to the other two fusion approaches.

关键词： Correlation Imaging Laplace equations Data integration Data models Standards Tuning

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A reduced-reference quality assessment scheme for blurred images

A reduced-reference quality assessment scheme for blurred im...

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IEEE Visual communications and image processing (VCIP)

作者： Zongxi Han Guangtao Zhai Yutao Liu Ke Gu Xinfeng Zhang Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Department of Computer Science Harbin Institute of Technology Harbin China School of Computer Science and Engineering Nanyang Technological University Singapore Rapid-Rich Object Search (ROSE) Lab Nanyang Technological University Singapore

ISBN: (纸本)9781509053179

In this paper, we propose a reduced-reference scheme for evaluating the quality of blurred images under the theory of free-energy principle. Specifically, the free-energy principle indicates that the brain tries to account for the input image with an internal generative model and the discrepancy between the image and its model-explained version, which can be measured by free energy, is related to the image's perceptual quality. Accordingly, we define a visual distance between the blurred image and its original image in free energy to evaluate the quality of the blurred image. Therefore, the proposed quality scheme belongs to reduced-reference methods, which needs some information from the original image for quality assessment. Experimental results on public databases, LIVE, TID2013 and C-SIQ, demonstrate the proposed method works in high consistency with subjective assessment results and outperforms representative image quality assessment approaches.

关键词： Brain modeling image quality Mathematical model Quality assessment Dictionaries Energy measurement

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ICFHR2016 Competition on the Analysis of Handwritten Text in images of Balinese Palm Leaf Manuscripts

ICFHR2016 Competition on the Analysis of Handwritten Text in...

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International Workshop on Frontiers in Handwriting Recognition

作者： Jean-Christophe Burie Mickael Coustaty Setiawan Hadi Made Windu Antara Kesiman Jean-Marc Ogier Erick Paulus Kimheng Sok I Made Gede Sunarya Dona Valy Laboratoirc Informatique Image Interaction (L3i) University of La Rochelle La Rochelle Cedex 1 France Robotics Artificial Intelligence and Digital image (RAID) Laboratory University of Padjajaran Bandung Indonesia Universite de La Rochelle La Rochelle Aquitaine-Limousin-Poitou FR Department of Information and Communication Engineering Institute of Technology of Cambodia Cambodia Lab. of Cultural Inf. Ganesha Univ. of Educ. Singaraja Indonesia

ISBN: (纸本)9781509009824

This paper presents the results of the Competition on the Analysis of Handwritten Text in images of Balinese Palm Leaf Manuscripts that was organized in the context of the 15th International Conference on Frontiers in Handwriting Recognition (ICFHR-2016). This competition provides a suitable challenge for testing and evaluation of robustness for some methods, image features and descriptors which were already proposed for handwritten text analysis of document image. In this competition, three different challenges in document analysis of palm leaf manuscript images are proposed: Challenge 1: Binarization of Palm Leaf Manuscript images, Challenge 2: Query-by-Example Word Spotting on Palm Leaf Manuscript images, and Challenge 3: Isolated Character Recognition of Balinese Script in Palm Leaf Manuscript images. The first handwritten Balinese palm leaf manuscript dataset, the AMADI_LontarSet, is used for performance evaluation. This paper describes the competition details including the dataset creation and the ground truth construction, the evaluation measures used, a short description of each participant as well as the performance of the all submitted methods.

关键词： Text analysis Measurement Frequency modulation PSNR Feature extraction Testing Skeleton

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A novel method for the construction of evolutionary tree based on expectation maximization algorithm

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Journal of Computational and Theoretical Nanoscience 2016年第6期13卷 3799-3803页

作者： Chen, Zhihua Zhao, Yang Wang, Zhuo Kou, Zheng Li, Hanggao Key Lab. of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei430074 China Institute of Biomedical and Pharmaceutical Sciences College of Biological Engineering and Food Science Hubei University of Technology Wuhan Hubei430068 China College of Computer Science South-Central University for Nationalities Wuhan Hubei430074 China

The difference of protein sequence or protein structure can be used for the construction of molecular evolutionary tree or phytogenetic tree with certain hierarchy and topology. The divergent points in the tree suggest that new genes appeared or common ancestors varied. The lengths of the branch in the tree show the evolution distances between proteins when divergences happened. The analysis of protein evolution can propose some methods for molecular evolution of various species and can confirm the accurate status in the evolution history. Moreover, the molecular evolution tree of proteins is a strong evidence for species classification. In the paper, the trees are constructed by the regular express of protein sequences and the expectation maximization (EM) algorithm. The proposed method in the paper can be used conveniently and can give reliable results, which benefit the biology computing and sequence analysis. Copyright © 2016 American Scientific Publishers. All rights reserved.

关键词： Proteins

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Wavelet permutation entropy analysis of Ventricular Fibrillation and Sudden Cardiac Death ECG signals

Wavelet permutation entropy analysis of Ventricular Fibrilla...

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2015 International Industrial Informatics and Computer Engineering Conference(IIICEC 2015)

作者： Lei Yang Fengzhen Hou Jun Wang Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm School of Science China Pharmaceutical University

In this paper,we applied wavelet permutation entropy to analyze the Ventricular Fibrillation(VF) signals and Sudden Cardiac Death(SCD) signals for making an effective distinction from normal sinus rhythm(NSR) ***,three different ECG signals are decomposed by wavelet and reconstructed in each single *** highly discriminated frequency band will be chosen as our target ***,under the circumstances of different series length,embedding dimension and delay time,the main work is to distinguish the three ECG signals in different frequency bands based on the permutation entropy(PE).The results show that permutation entropy method can make a distinction between normal and abnormal ECG signals which aren't decomposed,but the effect of decomposing with wavelets is better *** the highest discriminated frequency band is from 15.625 Hz to 31.25 *** the point of different data length,embedding dimension and delay time,it was found that permutation entropy method have different effects and the findings may assist cardiac clinical diagnosis.

关键词： Ventricular Fibrillation Signals Sudden Cardiac Death Signals Wavelet Analysis Reconstruction Permutation Entropy

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Neonatal Pain Expression Recognition Based on LBP Features and Sparse Representation

Neonatal Pain Expression Recognition Based on LBP Features a...

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2015 World Conference on Control, Electronics and Electrical Engineering(WCEE 2015)

作者： Guanming Lu Mengying Chen Xiaonan Li Jiangsu Province Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing Children's Hospital Affiliated to Nanjing Medical University

Facial expressions are considered a reliable indicator in neonatal pain *** paper proposes a new neonatal pain expression recognition method,which utilizes the feature descriptors based on weighted Local Binary Pattern(LBP)and the classifier based on sparse ***,the normalized facial image is described using a feature vector,which is histogram sequence obtained by concatenating the weighted histograms of the LBP maps of all the local ***,the Principal Component Analysis(PCA)method is used to reduce the dimension of the feature ***,the classifier based on sparse representation is applied to classify test sample into four classes of facial expressions:calm,crying,moderate pain,severe *** objective of this study is to assist the clinicians in assessing neonatal pain by utilizing computer-based image analysis *** experimental results on neonate facial image database show the effectiveness of the proposed *** classification accuracy is up to 85.50%.

关键词： expression recognition,local binary pattern,sparse representation,neonate,pain expression.

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Heart Rate Monitoring During Physical Exercise Using Wrist-Type Photoplethysmographic (PPG) Signals

Heart Rate Monitoring During Physical Exercise Using Wrist-T...

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

作者： Amirhosein Khas Ahmadi Parsa Moradi Mahan Malihi Sajjad Karimi Mohammad B. Shamsollahi Shamsollahi Biomedical Signal and Image Processing Lab. (BiSIPL) Department of Electrical Engineering Sharif University of Technology Tehran Iran Biomedical Signal and Image Processing Lab. (BiSIPL) Department of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9781424492695

Heart rate monitoring using wrist-type using photoplethysmographic (PPG) signals during subjects' intensive exercises is a challenging problem, since signals are strongly affected by motion artifacts caused by unexpected movements. This paper presents a method that uses both time and frequency characteristics of signals;using sparse signal reconstruction for high-resolution spectrum estimation. Experimental results on type data sets recorded from 12 subjects during fast running at peak speed of 15 km/hour. The results have a performance with the average absolute error being 1.80 beat per minute.

关键词： Photoplethysmographic (PPG) Sparse Signal Reconstruction Regression Heart rate

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Beta Wave of Sleep Electroencephalogram Analysis Based on Multiscale Sign Series Entropy

Beta Wave of Sleep Electroencephalogram Analysis Based on Mu...

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2015 International Conference on Artificial Intelligence and Industrial Engineering(AIIE 2015)

作者： Jianhui Jiang Shitong Wang Fengzhen Hou Jun Wang Jin Li Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm School of Science China Pharmaceutical University College of Physics and Information Technology Shaanxi Normal University

ISBN: (纸本)9781510806450

Sleep Electroencephalogram(Sleep EEG) detection and treatment can provide the basis for clinical diagnosis and treatment. According to the non-stationary random character of EEG itself, the paper proposed multiscale sign series entropy(MSSE) method and applied it to the state of sleep EEG analysis. Numerical results showed that, MSSE method can effectively differentiate awake period β wave and sleep stage β wave even if under the influence of the noise. The results show that the algorithm can aid in clinical diagnosis of sleep EEG.

关键词： sleep electroencephalogram multiscale sign series entropy clinical diagnosis

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