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HIGHER-OrdER SPATIAL STATISTICS: A STRONG ALTERNATIVE IN image PROCESSING 3

HIGHER-ORDER SPATIAL STATISTICS: A STRONG ALTERNATIVE IN IMA...

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3rd International conference on pattern analysis and Intelligent Systems (PAIS)

作者： Boulemnadjel, Amel Kaabache, Ferhat Kharfouchi, Soumeya Hachouf, Fella Univ Constantine1 Fac Sci & Technol Lab Automat & Robot Constantine Algeria Univ Constantine 3 Fac Med Constantine Algeria

ISBN: (纸本)9781538642382

The goal of the this work is twofold, namely: 1) to discuss a method for identification of linear 2-D nonminimum phase system using higher order cumulants alone, and to derive the almost-sure convergence properties of sample estimates of higher-order spatial statistics. The measure of almost sure convergence is obtained for the sample estimates of third and fourth order moments and cumulants. 2) The experimental cumulants are used as texture features, they are later, incorporated into classification schemes. Some applications using synthetic textures and aerial images are presented to illustrate great descriptive power of higher-order cumulants.

关键词： Higher-order spatial statistics Nonminimum phase Classification Almost sure covergence

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Grey Level Co-occurrence Matrix (GLCM) as a Radiomics Feature for Artificial Intelligence (AI) Assisted Positron Emission Tomography (PET) images analysis 3

Grey Level Co-occurrence Matrix (GLCM) as a Radiomics Featur...

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2019 3rd International conference on Artificial Intelligence Applications and Technologies, AIAAT 2019

作者： Tamal, Mahbubunnabi Imam Abdulrahman Bin Faisal University PO Box 1982 Dammam31441 Saudi Arabia

Positron Emission Tomography (PET) allows tumour microenvironment to be studied in vivo with high sensitivity and specificity. Inter- and intra-tumour morphological and phenotypic heterogeneity or pattern provided by PET images are of critical importance. The traditional practice of visual interpretation of these images are not sufficient enough to extract all the information embedded in the images. On the other hand, simultaneous development of automated and reproducible analysis methodologies makes it possible to extract large amount of quantitative features from these images which is termed as radiomics. analysis of these radiomics feature using artificial intelligence (AI) can significantly improve individualized treatment selection and monitoring. Grey level co-occurrence matrix (GLCM), a member of texture based radiomics feature family is widely used as a biomarker of heterogeneity and can provide information of the tumour microenvironment. The GLCM can subsequently be used for artificial intelligence (AI) assisted tumour diagnosis, monitoring of progression and treatment planning as well as for monitoring response to therapeutic intervention. This aim of the study was to investigate the accuracy and robustness of PET based GLCM in varying image acquisition and analysis conditions using phantom data. It has been observed that GLCM based textural features (e.g., correlation, entropy, homogeneity, energy contrast and dissimilarity) are not only dependent on the volume but also on the quantization level. They are also dependent on signal-to-noise ratio (SNR) and image contrast. The dependencies of these features to the varying imaging conditions are also not linear and cannot always be directly related. To use these GLCM derived textural features as biomarkers for AI assisted analysis, all the information regarding the textural features should always be included along with the changes in volumes and contrast of the PET images in the training dataset. © 2019 IOP P

关键词： Positron emission tomography

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Brain Tissue Segmentation Using NeuroNet With Different Pre-processing Techniques

Brain Tissue Segmentation Using NeuroNet With Different Pre-...

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International conference on Informatics, Electronics & Vision (ICIEV)

作者： Fakrul Islam Tushar Basel Alyafi Md. Kamrul Hasan Lavsen Dahal Erasmus+ Joint Master In Medical Imaging and Applications University of Girona Spain

Automatic segmentation of brain Magnetic Resonance Imaging (MRI) images is one of the vital steps for quantitative analysis of brain for further inspection. In this paper, NeuroNet has been adopted to segment the brain tissues (white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF)) which uses Residual Network (ResNet) in encoder and Fully Convolution Network (FCN) in the decoder. To achieve the best performance, various hyper-parameters have been tuned, while, network parameters (kernel and bias) were initialized using the NeuroNet pre-trained model. Different pre-processing pipelines have also been introduced to get a robust trained model. The model has been trained and tested on IBSR18 data-set. To validate the research outcome, performance was measured quantitatively using Dice Similarity Coefficient (DSC) and is reported on average as 0.84 for CSF, 0.94 for GM, and 0.94 for WM. The outcome of the research indicates that for the IBSR18 data-set, pre-processing and proper tuning of hyper-parameters for NeuroNet model have improvement in DSC for the brain tissue segmentation.

关键词： Histograms image segmentation Pipelines Training Brain modeling Feature extraction

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Invited Talk 1: Gait-based Age analysis

Invited Talk 1: Gait-based Age Analysis

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International conference on Informatics, Electronics & Vision (ICIEV)

作者： Yasushi Makihara Mayu Okumura Haruyuki Iwama Chi Xu Xiang Li Atsuya Sakata Gakuto Ogi Noriko Takemura Daigo Muramatsu Yasushi Yagi The Institute of Scientific and Industrial Research Osaka University Japan School of Computer Science and Engineering Nanjing University of Science and Technology China The Institute of Datability Science Osaka University Japan

In this keynote talk, we will describe a series of our work on gait-based age analysis. First, we will introduce a former version of our gait database for gait-based age estimation and its initial performance evaluation using a standard gait features such as gait energy image (GEI) and frequency-domain feature as well as a standard regression method, i.e., Gaussian process regression [1]. We will then introduce our recent, the world largest gait databases for age estimation [3] which were collected in conjunction with long-term exhibition of intuitive video-based gait analysis demonstration in a science museum [2]. We also introduce performance evaluation results of several benchmark methods of gait-based age estimation on it. Thereafter, we introduce two recent approaches to gait-based age estimation. One is a hierarchical approach, where a gait sample is projected into a low-dimensional embedding space, then classified into one of age groups by a directed acyclic graph support vector machine, and finally fed into an age group-dependent support vector regressor to estimate its age [4]. The other one is based on a state-of-the-art deep learning architecture, i.e., DenseNet [5]. In addition to the above-mentioned gait-based age estimation, we also construct an age progression/regression model based on mean GEI of each age group and free-form deformation between the mean GEIs of adjacent age groups [6], which has potential applications to gait recognition robust against time elapse and physical gait age estimation.

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Performance analysis of Real Time image Processing for Lightning Event Using Cython and Python Programming Languages 3

Performance Analysis of Real Time Image Processing for Light...

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3rd International conference on Science and Technology Applications in Climate Change, STACLIM 2018

作者： Mun, S.H. Ahmad, M.R. Malik, R.F. Esa, M.R.M. Sabri, M.H.M. Periannan, D. Seah, B.Y. Mohamad, S.A. Cooray, V. Alkahtani, A.A. Kadir, M.Z.A.A.B. Fakulti Kejuruteraan Elektronik Dan Kejuruteraan Komputer Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Inderalaya Sumatera Selatan Indonesia Skudai Johor Bharu81310 Malaysia Ångström Laboratory Division for Electricity Department of Engineering Sciences Uppsala University Box 534 S-75121 Sweden Jalan IKRAM-UNITEN Kajang Selangor43000 Malaysia

Due to the uncommon and unpredictable phenomenon, lightning event become one of the challenging research topics for the scientist. The main thing that most capturing their attention is the lightning detection method which can improve the public safety by pinpointing the location of the lightning event and sending out the warning notification. Currently, there are lots of researcher proceed with the study related with the relationship between the pattern of the lightning and the electromagnetic signal received. During the research process, they would like to capture a clear lightning image from the cloud to proceed with the study of characteristic of the pattern of the lightning and electromagnetic field. The most common method for the researcher to detect and capture the lightning image is using the high-speed camera with a flash sensor. But this method is extremely expensive and has its own limitation on mobility part. Therefore, this paper is to develop and evaluate the suitable algorithm and programming language onto Raspberry Pi platform and act as an upgraded choice to replace high speed camera with affordable cost, high mobility but less accuracy compares to high speed camera. © 2019 Published under licence by IOP Publishing Ltd.

关键词： High speed cameras

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Human and Machine recognition of Transportation Modes from Body-Worn Camera images

Human and Machine Recognition of Transportation Modes from B...

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International conference on Informatics, Electronics & Vision (ICIEV)

作者： Sebastien Richoz Mathias Ciliberto Lin Wang Philip Birch Hristijan Gjoreski Andres Perez-Uribe Daniel Roggen Wearable Technologies Lab University of Sussex Brighton United Kingdom Centre for Intelligent Sensing Queen Mary University London London United Kingdom Engineering and Informatics University of Sussex Brighton United Kingdom Faculty of Electrical Engineering and Information Technologies Ss. Cyril and Methodius University Skopje Macedonia Institute for Information and Communication Technologies University of Applied Sciences Yverdon Switzerland

Computer vision techniques applied on images opportunistically captured from body-worn cameras or mobile phones offer tremendous potential for vision-based context awareness. In this paper, we evaluate the potential to recognise the modes of locomotion and transportation of mobile users, by analysing single images captured by body-worn cameras. We evaluate this with the publicly available Sussex-Huawei Locomotion and Transportation Dataset, which includes 8 transportation and locomotion modes performed over 7 months by 3 users. We present a baseline performance obtained through crowd sourcing using Amazon Mechanical Turk. Humans infered the correct modes of transportations from images with an F1-score of 52%. The performance obtained by five state-of-the-art Deep Neural Networks (VGG16, VGG19, ResNet50, MobileNet and DenseNet169) on the same task was always above 71.3% F1-score. We characterise the effect of partitioning the training data to fine-tune different number of blocks of the deep networks and provide recommendations for mobile implementations.

关键词： Task analysis Cameras Automobiles Biomedical monitoring image recognition Sensors

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Mean of Means: An Automatic Liver Segmentation Algorithm

Mean of Means: An Automatic Liver Segmentation Algorithm

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International conference on Informatics, Electronics & Vision (ICIEV)

作者： Laramie Paxton Yufeng Cao Kevin Vixie Yuan Wang Chaan Ng Brian Hobbs Department of Math & Natural Sciences Marian University Fond du Lac Wisconsin U.S.A. Department of Mathematics & Statistics Washington State University Pullman Washington U.S.A. M.D. Anderson Cancer Center University of Texas Houston Texas U.S.A. Cleveland Clinic Cleveland Ohio U.S.A.

We present an automatic liver segmentation method that utilizes the time series data in conjunction with the BK graph cut algorithm and uses a novel approach of computing a “mean of means” for each of the sample healthy and tumor tissue intensities from a set of different tumors. Thus, there is no training process required since these are computed ahead of time using Regions of Interest provided by radiologists. We also use a Gaussian B-spline to fit these two vector means to curves as an approximation for the intensity signals for the healthy and tumor tissues. This method provides a reasonable degree of accuracy for an automatic segmentation scheme, yielding a mean Dice similarity coefficient (DSC) of 73 percent, a relative volume difference (RVD) of 19.5 percent, and a Jaccard Score (JS) of 58.5 percent. The algorithm is simple to implement computationally, and the mean runtime of 24 seconds is short given that no training process is needed.

关键词： Tumors image segmentation Liver Time series analysis Splines (mathematics) Training Computed tomography

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Non-destructively Prediction of Quality Parameters of Dry-Cured iberian Ham by Applying Computer Vision and Low-Field MRI 9th

Non-destructively Prediction of Quality Parameters of Dry-Cu...

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9th iberian conference on pattern recognition and image analysis, IbPRIA 2019

作者： Torres, Juan Pedro Ávila, Mar Caro, Andrés Pérez-Palacios, Trinidad Caballero, Daniel Media Engineering Group School of Technology University of Extremadura Av. Ciencias S/N Cáceres10003 Spain Food Technology Department Research Institute of Meat and Meat Product University of Extremadura Av. Ciencias S/N Cáceres10003 Spain Chemometrics and Analytical Technology Department of Food Science University of Copenhagen Rolighedsvej 26 Frederiksberg1958 Denmark

ISBN: (纸本)9783030313319

Computer vision algorithms and Magnetic Resonance Imaging (MRI) have been proposed to obtain quality traits of iberian hams, due to the non-destructive, non-ionizing and innocuous nature of these approaches. However, all the proposals have been based on high-field MRI scanners, which obtain high quality images but also involve very high economical costs. In this paper, low-field MRI devices and three classical texture algorithms were used to predict quality traits of iberian ham. Prediction equation of quality features were obtained, which estimate the quality parameters as a function of computational textures. The texture features were obtained by applying three well-known classical texture algorithms (GLCM - Gray Level Co-occurrence Matrix, GLRLM - Gray Level Run Length Matrix and NGLDM - Neighbouring Gray Level Dependence Matrix) on low-field MRI. Being the first approach that exploits this type of scanner for this purpose in dry-cured meat, the predicted elements were compared and correlated to the results obtained by means of traditional physico-chemical methods. The obtained correlation were higher than 0.7 for almost all the quality traits, reached very good to excellent relationship. These high correlations between both sets of data (traditional and estimated results) prove that low-field MRI combined with texture algorithms could be used to estimate the quality traits of meat products in a non-destructive and efficient way. © 2019, Springer Nature Switzerland AG.

关键词： Forecasting

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Classification and Change Detection Using Multi-periodic Harmonic analysis 2019

Classification and Change Detection Using Multi-periodic Har...

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Proceedings of the 3rd International conference on Vision, image and Signal Processing

作者： Myunghee Jun Sang-Hoon Lee Software Engineering Anyang University Kyunggi-do South Korea Industrial & Business Engineering Gachon University Kyunggi-doSouth Korea

ISBN: (纸本)9781450376259

Time-series of satellite images have been used to identify and monitor land cover change. Long-term datasets are very useful to examine an area over a period and see what changes have occurred. It is not an easy task to develop satisfactory change detection algorithms due to the processing complexity and extraction of meaningful change pattern of interest. In an effort to find an appropriate approach for this challenge, this paper presents a harmonic model-based change detection method using time- series of satellite images. The proposed algorithm is based on the temporal profile over time for the long-term change rather than a temporary change. A harmonic model can characterize the temporal variability of land covers whose signatures exhibit seasonal trends since components of the harmonic function inherently contain temporal information about seasonal changes. Several experiments were conducted on a multi-temporal dataset of Moderate Resolution Imaging Spectroradiometer (MODIS) over the Korean peninsula, in the time interval of 2012-2016. The results indicate that the proposed algorithm has a great potential for monitoring land cover condition and annual long-term landcover change over large regions.

关键词： Harmonic Model Land Cover Classification MODIS Change Detection

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Using deep convolutional neural networks with adaptive activation functions for medical CT brain image Classification 25

Using deep convolutional neural networks with adaptive activ...

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25th Iranian conference on Biomedical Engineering / 3rd International Iranian conference on Biomedical Engineering (ICBME)

作者： Zahedinasab, Roxana Mohseni, Hadis Shahid Bahonar Univ Kerman Comp Engn Dept Kerman Iran

ISBN: (纸本)9781538679524

recently, imaging has become an essential component in many fields of medical research. analysis of the diverse medical image types requires sophisticated visualization and processing tools. Deep neural networks have introduced themselves as one of the most important branches of machine learning and have been successfully used in many fields of pattern recognition and medical imaging applications. Among the different networks, convolutional neural networks which are biologically inspired variants of multilayer perceptions are widely used in the medical imaging field. In these networks, activation function plays a significant role especially when the data come in different scales. There is a hope to improve the performance of these networks by using adaptive activation functions which adapts their parameters to the input data. In this paper, we have used a modified version of a successful convolutional neural network tuned for medical image classification and investigated the effect of applying three types of adaptive activation functions on that. These activation functions combine basic activation functions in linear (mixed) and nonlinear (gated and hierarchical) ways. The effectiveness of using these adaptive functions is shown on a CT brain images dataset (as a complex medical dataset). The experiments show that the classification accuracy of the proposed network with adaptive activation functions is higher compared to the ones using basic activation functions.

关键词： deep learning Convolutional neural networks Adaptive activation functions CT brain images image classification

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