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Pharmaco-fUS: Quantification of pharmacologically-induced dynamic changes in brain perfusion and connectivity by functional ultrasound imaging in awake mice

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NEUROIMAGE 2020年 222卷 117231-117231页

作者： Rabut, Claire Ferrier, Jeremy Bertolo, Adrien Osmanski, Bruno Mousset, Xavier Pezet, Sophie Deffieux, Thomas Lenkei, Zsolt Tanter, Mickael PSL Res Univ Paris CNRS ESPCI Paris INSERMPhys Med Paris Cnrs France Univ Paris Inst Psychiat & Neurosci Paris INSERM U1266 102 Rue Sante F-75014 Paris France Iconeus 6 Rue Calvin F-75005 Paris France

There is a critical need for reliable quantitative biomarkers to assess functional brain alterations in mouse models of neuropsychiatric diseases, but current imaging methods measuring drug effects through the neurovascular coupling, face issues including poor sensitivity, drug-induced changes in global brain perfusion and the effects of anesthesia. Here we demonstrate the proof-of-concept of a minimally-invasive fUS imaging approach to detect the acute cholinergic modulatory effects of Scopolamine (ScoP) on functional brain connectivity in awake and behaving mice, through the intact skull. A machine-learning algorithm constructed an ad-hoc pharmacological score from the ScoP-induced changes in connectivity patterns of five mice. The discrimination model shows important ScoP-induced increase of the hippocampo-cortical connectivity. The pharmacological score led to robust discrimination of ScoP treatment from baseline in an independent dataset and showed, in another independent group, dose-dependent specific effects of central cholinergic modulation of functional connectivity, independent from global brain perfusion changes. In conclusion, we introduce pharmaco-fUS as a simple, robust, specific and sensitive modality to monitor drug effects on perfusion and functional connectivity in the awake mouse brain.

关键词： Functional ultrasound imaging Awake imaging Functional biomarker support vector machine classifier Drug effects monitoring

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THERMAL FACE RECOGNITION BASED ON PHYSIOLOGICAL INFORMATION 26

THERMAL FACE RECOGNITION BASED ON PHYSIOLOGICAL INFORMATION

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26th IEEE International Conference on Image Processing (ICIP)

作者： Lin, Shinfeng D. Chen, Kuanyuan Chen, Wensheng Natl Dong Hwa Univ Dept Comp Sci & Informat Engn Hualien Taiwan

ISBN: (纸本)9781538662496

In this paper, we propose a novel thermal face recognition based on physiological information. The training phase includes preprocessing, feature extraction and classification. In the beginning, the human face can be depicted from the background of thermal image using the Bayesian framework and normalized to uniform size. A grid of 22 thermal points is extracted as a feature vector. These 22 extracted points are used to train Linear support vector machine classifier (linear SVC). The classifier calculates the support vectors and uses them to find the hyperplane for classification. A feature vector of testing image is inputted to the classifier for face recognition. Our contribution is that the proposed method firstly applies temperature information in face recognition. Experimental results prove the effectiveness of the proposed method.

关键词： Face Recognition Thermal Image Bayesian Framework support vector machine classifier

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THERMAL FACE RECOGNITION UNDER DISGUISED CONDITIONS 18

THERMAL FACE RECOGNITION UNDER DISGUISED CONDITIONS

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International Conference on machine Learning and Cybernetics (IMLC)

作者： Lin, Shinfeng D. Chen, Kuanyuan Dept Comp Sci & Informat Engn Hualien Taiwan Natl Dong Hwa Univ Hualien Taiwan

ISBN: (纸本)9781728128160

A thermal face recognition under disguised conditions using model fusion is proposed. The proposed model fusion has three main approaches: Linear support vector machine classifier (Linear SVC), Convolutional Neural Network (CNN) and Ordinary Least Square (OLS). A grid of 22 thermal face points based on physiological information is extracted for training Linear SVC. The support vectors of testing images using Linear SVC are calculated to find the hyperplane for classification. The novelty is that we firstly apply temperature information in face recognition. In CNN model, the multiple layers of convolutional layer are utilized to extract more effective features. The fully connected layer (FC layer) is trained using the feature matrix of the last convolutional layer. This FC layer is then a classifier to identify the category of the test image. In the training phase, the predicted values from above two approaches are provided to the OLS fir linear regression. The OLS assigns weighting values to these two approaches. This can effectively compensate for the advantages and disadvantages of two approaches. In addition to the comparison with the traditional thermal face recognition, an experiment under disguised conditions was conducted. Experimental results of the proposed method outperform the existing methods.

关键词： Thermal face recognition Thermal image support vector machine classifier Convolutional neural network Ordinary least square

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Significance of Higher-Order Spectral Analysis in Infant Cry Classification

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2018年第1期37卷 232-254页

作者： Chittora, Anshu Patil, Hemant A. Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

In this paper, higher-order spectral analysis is applied to infant cry signals for classification of normal infant cries from pathological infant cries. From the family of higher-order spectra, bispectrum is considered for the proposed task. Bispectrum is the Fourier transform of the third-order cumulant function. To extract features from the bispectrum, application of higher-order singular value decomposition theorem is proposed. Experimental results show the average classification accuracy of 82.44 +/- 4.03% and Matthew's correlation coefficient (MCC) of 0.62 with proposed bispectrum features. In all of the experiments reported in this paper, support vector machine with radial basis function kernel is used as the pattern classifier. Performance of the proposed features is also compared with the state-of-the-art methods such as linear frequency cepstral coefficients, Mel frequency cepstral coefficients, perceptual linear prediction coefficients, linear prediction coefficients, linear prediction cepstral coefficients and perceptual linear prediction cepstral coefficients, and is found to be better than that given by these feature sets. The proposed bispectrum-based features are shown to be robust under signal degradation or noisy conditions at various SNR levels. Performance in the presence of noise is compared with the state-of-the-art spectral feature sets using MCC scores. In addition, effectiveness of cryunit segmentation in normal and pathological infant cry classification task is reported.

关键词： Bispectrum Cumulants Higher-order singular value decomposition theorem Higher-order spectral analysis support vector machine classifier

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Computer-aided diagnosis of cataract using deep transfer learning

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2019年第0期53卷 101533-000页

作者： Pratap, Turimerla Kokil, Priyanka Indian Inst Informat Technol Design & Mfg Dept Elect & Commun Engn Chennai 600127 Tamil Nadu India

Cataract is a leading eye disease across the world. If cataract is not diagnosed in earlier stage, then it may lead to blindness. Earlier detection is the best way to control the risk and to avoid painful surgery. Thus, this paper mainly focuses on cataract detection from fundus retinal images. A computer-aided automatic cataract detection method is proposed to detect various stages of the cataract such as normal, mild, moderate, and severe from the fundus images. The proposed method uses the pre-trained convolutional neural network (CNN) for the transfer learning to carry out automatic cataract classification. Then pre-trained CNN model is used for the feature extraction and the extracted features are then applied to a support vector machine (SVM) classifier. The fundus cataract images are collected from the various open access datasets and labelled into four stages with the help of ophthalmologic experts. The four stage classification accuracy obtained is 92.91%. Since, the image quality is important in CNN, an image quality selection module is incorporated to decide the quality of fundus image for diagnosis. The revaluation of results based on the quality of fundus images is also presented. Based on the results, the proposed method proved to be an efficient method that uses pre-trained CNN as transfer learning for the classification of the cataract. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Computer-aided diagnosis Pre-trained convolutional neural networks Feature extraction support vector machine classifier Transfer learning

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Time-frequency analysis of PD-induced UHF signal in GIS and feature extraction using invariant moments

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第2期12卷 169-175页

作者： Li, Xi Wang, Xiaohua Xie, Dingli Wang, Xinqiao Yang, Aijun Rong, Mingzhe Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia

The ultra-high-frequency (UHF) method is efficient in partial discharges (PDs) detection in gas-insulated switchgear (GIS). The features extraction of UHF signals is significant for propagation characteristics analysis and PD pattern classification. The PD-induced UHF signals are acquired by the internal UHF sensors in an actual 252kV L-shaped GIS. The short-time Fourier transform method is applied to process UHF signals and describe the propagation characteristics in L-shaped GIS. Hu's invariant moments of energy density distribution are extracted as features in time-frequency plane. The features are utilised to discriminate different PD defect patterns in actual GIS model by the support vector machine classifier and achieve good results. Finally, a novel system of features extraction and classification of UHF signals is summarised.

关键词： time-frequency analysis gas insulated switchgear partial discharge measurement feature extraction signal detection signal classification UHF detectors UHF measurement Fourier transforms support vector machines Time-frequency analysis PD-induced UHF signal detection feature extraction ultrahigh-frequency method partial discharge detection gas-insulated switchgear propagation characteristics analysis PD pattern classification PD-induced UHF signal acquisition internal UHF sensor L-shaped GIS short-time Fourier transform method UHF signal processing Hu invariant moment energy density distribution extraction support vector machine classifier UHF signal classification voltage 252 kV

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Biomedical compound figure detection using deep learning and fusion techniques

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IET IMAGE PROCESSING 2018年第6期12卷 1031-1037页

作者： Lee, Sheng Long Zare, Mohammad Reza Monash Univ Malaysia Sch Informat Technol Jalan Lagoon Selatan Bandar Sunway 47500 Subang Jaya Malaysia

Images contain significant amounts of information but present different challenges relative to textual information. One such challenge is compound figures or images made up of two or more subfigures. A deep learning model is proposed for compound figure detection (CFD) in the biomedical article domain. First, pre-trained convolutional neural networks (CNNs) are selected for transfer learning to take advantage of the image classification performance of CNNs and to overcome the limited dataset of the CFD problem. Next, the pre-trained CNNs are fine-tuned on the training data with early-stopping to avoid overfitting. Alternatively, layer activations of the pre-trained CNNs are extracted and used as input features to a support vector machine classifier. Finally, individual model outputs are combined with score-based fusion. The proposed combined model obtained a best test accuracy of 90.03 and 96.93% outperforming traditional hand-crafted and other deep learning representations on the ImageCLEF 2015 and 2016 CFD subtask datasets, respectively, by using AlexNet, VGG-16, VGG-19 pre-trained CNNs fine-tuned until best validation accuracy stops increasing combined with the combPROD score-based fusion operator.

关键词： medical image processing feature extraction neural nets support vector machines image classification image fusion learning (artificial intelligence) biomedical compound figure detection deep learning model compound figure detection CFD pre-trained convolutional neural networks CNN transfer learning image classification support vector machine classifier score-based fusion

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Hot spot method for pedestrian detection using saliency maps, discrete Chebyshev moments and support vector machine

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IET IMAGE PROCESSING 2018年第7期12卷 1284-1291页

作者： Lahouli, Ichraf Karakasis, Evangelos Haelterman, Robby Chtourou, Zied De Cubber, Geert Gasteratos, Antonios Attia, Rabah Mil Acad Tunisia VRIT Res Grp Nabeul 8000 Tunisia Royal Mil Acad Ave Renaissance 30 B-1000 Brussels Belgium Tunisia Polytech Sch SERCOM Lab Rue El Khawarizmi Tunis 2078 La Marsa Tunisia Democritus Univ Thrace Vasilissis Sofias 12 GR-67100 Xanthi Greece

The increasing risks of border intrusions or attacks on sensitive facilities and the growing availability of surveillance cameras lead to extensive research efforts for robust detection of pedestrians using images. However, the surveillance of borders or sensitive facilities poses many challenges including the need to set up many cameras to cover the whole area of interest, the high bandwidth requirements for data streaming and the high-processing requirements. Driven by day and night capabilities of the thermal sensors and the distinguished thermal signature of humans, the authors propose a novel and robust method for the detection of pedestrians using thermal images. The method is composed of three steps: a detection which is based on a saliency map in conjunction with a contrast-enhancement technique, a shape description based on discrete Chebyshev moments and a classification step using a support vector machine classifier. The performance of the method is tested using two different thermal datasets and is compared with the conventional maximally stable extremal regions detector. The obtained results prove the robustness and the superiority of the proposed framework in terms of true and false positives rates and computational costs which make it suitable for low-performance processing platforms and real-time applications.

关键词： pedestrians support vector machines reliability video surveillance cameras image sensors temperature sensors temperature measurement infrared imaging image enhancement image classification hot spot method pedestrian detection saliency map discrete Chebyshev moment border intrusion surveillance camera data streaming thermal sensor thermal signature thermal imaging contrast-enhancement technique shape description support vector machine classifier image classification maximally stable extremal region detector

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Maximization of regional probabilities using Optimal Surface Graphs: Application to carotid artery segmentation in MRI

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MEDICAL PHYSICS 2018年第3期45卷 1159-1169页

作者： Lorza, Andres M. Arias van Engelen, Arna Petersen, Jens van der Lugt, Aad de Bruijne, Marleen Erasmus MC Biomed Imaging Grp Rotterdam Dept Radiol & Med Informat Rotterdam Netherlands Univ Copenhagen Dept Comp Sci Copenhagen Denmark Erasmus MC Dept Radiol Rotterdam Netherlands

PurposeWe present a segmentation method that maximizes regional probabilities enclosed by coupled surfaces using an Optimal Surface Graph (OSG) cut approach. This OSG cut determines the globally optimal solution given a graph constructed around an initial surface. While most methods for vessel wall segmentation only use edge information, we show that maximizing regional probabilities using an OSG improves the segmentation results. We applied this to automatically segment the vessel wall of the carotid artery in magnetic resonance images. MethodsFirst, voxel-wise regional probability maps were obtained using a support vector machine classifier trained on local image features. Then, the OSG segments the regions which maximizes the regional probabilities considering smoothness and topological constraints. ResultsThe method was evaluated on 49 carotid arteries from 30 subjects. The proposed method shows good accuracy with a Dice wall overlap of 74.14.3%, and significantly outperforms a published method based on an OSG using only surface information, the obtained segmentations using voxel-wise classification alone, and another published artery wall segmentation method based on a deformable surface model. Intraclass correlations (ICC) with manually measured lumen and wall volumes were similar to those obtained between observers. Finally, we show a good reproducibility of the method with ICC=0.86 between the volumes measured in scans repeated within a short time interval. ConclusionsIn this work, a new segmentation method that uses both an OSG and regional probabilities is presented. The method shows good segmentations of the carotid artery in MRI and outperformed another segmentation method that uses OSG and edge information and the voxel-wise segmentation using the probability maps. (c) 2018 American Association of Physicists in Medicine

关键词： carotid artery graph cut maximization of regional probabilities MRI Optimal Surface Graph segmentation support vector machine classifier

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Semi-automatic leaf disease detection and classification system for soybean culture

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IET IMAGE PROCESSING 2018年第6期12卷 1038-1048页

作者： Kaur, Sukhvir Pandey, Shreelekha Goel, Shivani Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala 147004 Punjab India Bennett Univ Comp Sci & Engn Dept Greater Noida UP India

Development of automatic disease detection and classification system is significantly explored in precision agriculture. In the past few decades, researchers have studied several cultures exploiting different parts of a plant. A similar study is performed for Soybean using leaf images. A rule based semi-automatic system using concepts of k-means is designed and implemented to distinguish healthy leaves from diseased leaves. In addition, a diseased leaf is classified into one of the three categories (downy mildew, frog eye, and Septoria leaf blight). Experiments are performed by separately utilising colour features, texture features, and their combinations to train three models based on support vector machine classifier. Results are generated using thousands of images collected from PlantVillage dataset. Acceptable average accuracy values are reported for all the considered combinations which are also found to be better than existing ones. This study also attempts to discover the best performing feature set for leaf disease detection in Soybean. The system is shown to efficiently compute the disease severity as well. Visual examination of leaf samples further proves the suitability of the proposed system for detection, classification, and severity calculation.

关键词： plant diseases image classification agricultural engineering image colour analysis image texture support vector machines food products feature extraction semiautomatic leaf disease detection and classification system soybean culture automatic disease detection and classification system precision agriculture leaf image k-means concepts colour feature texture feature support vector machine classifier PlantVillage dataset feature set

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