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International conference on Computational Intelligence and Data Science (ICCIDS)

作者： Priyanka Kumar, Dharmender Guru Jambheshwar Univ Sci & Technol GJU S&T Hisar 125001 Haryana India

Medical imaging applications in hospitals and laboratories have shown benefits in visualizing patient's body for diagnosis and treatment of disease. Ultrasound (US) is considered as safest medical imaging technique and is therefore used extensively in medical and healthcare using computer aided system. However presence of some artifacts due to patient mobility and equipment limitations makes diagnosis of these US images difficult. There is need for some pre-processing methods to improve quality of images for the purpose of classification and segmentation while preserving pixels of interest. These pixels contain information about images known as image features which forms the data model for classification. So, feature extraction and selection is important phase in classification step of diagnostic system. Keeping this in mind, this study focuses on preprocessing and feature extraction and selection phase of ultrasound images of kidney for making a classification model. Four operations cropping, interpolation, rotation and background removal are applied as preprocessing methods to enhance the quality of images and for making diagnosis easy and effective. Afterwards, a number of second order statistical texture features including energy, entropy, homogeneity, correlation, contrast, dissimilarity are generated using GLCM. Finally obtained features are reduced to optimal subset using principal component analysis (PCA). The results show that GLCM in combination with PCA for feature reduction gives high classification accuracy when classifying images using Artificial Neural Network (ANN). (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Pre-processing Gray Level Co-occurrence Matrix Principal Component Analysis Feature reduction

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Intra cerebral haemorrhage (ICH) segmentation using CT scan image texture analysis

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AIP conference Proceedings 2023年第1期2738卷

作者： Andi Kurniawan Nugroho Budiani Destyningtias Sri Heranurweni Dinar Mutiara Kusumo Nugraheni Electrical Engineering Department Engineering Faculty Universitas Semarang Tlogosari Semarang Indonesia Computer Science Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

In recent decades, the automatic segmentation of medical images has become necessary for many researchers. Pre-processing and computer graphic techniques are a big part of processing medical data and changing the format to make it understandable. The problem of this study is how to segment each slice of Computed Tomography (CT) image based on the pixel features of suspect stroke images, especially in intracerebral type strokes. The purpose of this study was to segment based on the feature pixel features based on the statistical data of suspect Intra Cerebral Haemorrhage (ICH) features so that normal and abnormal tissue characteristics could be seen due to bleeding. This research method develops segmentation based on statistical features. CT images require pre-processing (pre-processing) to image enhancement to meet the CT image requirements with isolation, region growing, and logical operators (OR and AND). Morphological methods (opening and closing) with logical operators produce good results. These results can help that the most uncomplicated ICH stroke segmentation process, the thresholding process, is used to extract the ICH stroke regions from the brain CT images. A median filter is applied to remove noise from the image. This study used statistical features to calculate the first-order histogram to detect the ICH stroke area.

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stochastic solutions for linear inverse problems using the prior implicit in a denoiser 21

Stochastic solutions for linear inverse problems using the p...

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Proceedings of the 35th International conference on Neural Information processing Systems

作者： Zahra Kadkhodaie Eero P. Simoncelli Center for Data Science New York University Center for Neural Science and Courant Inst. of Mathematical Sciences New York University Flatiron Institute Simons Foundation

ISBN: (纸本)9781713845393

Deep neural networks have provided state-of-the-art solutions for problems such as image denoising, which implicitly rely on a prior probability model of natural images. Two recent lines of work – Denoising Score Matching and Plug-and-Play – propose methodologies for drawing samples from this implicit prior and using it to solve inverse problems, respectively. Here, we develop a parsimonious and robust generalization of these ideas. We rely on a classic statistical result that shows the least-squares solution for removing additive Gaussian noise can be written directly in terms of the gradient of the log of the noisy signal density. We use this to derive a stochastic coarse-to-fine gradient ascent procedure for drawing high-probability samples from the implicit prior embedded within a CNN trained to perform blind denoising. A generalization of this algorithm to constrained sampling provides a method for using the implicit prior to solve any deterministic linear inverse problem, with no additional training, thus extending the power of supervised learning for denoising to a much broader set of problems. The algorithm relies on minimal assumptions and exhibits robust convergence over a wide range of parameter choices. To demonstrate the generality of our method, we use it to obtain state-of-the-art levels of unsupervised performance for deblurring, super-resolution, and compressive sensing.

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Performance Analysis of Stego images Detection Using Shallow Denoising Autoencoders

Performance Analysis of Stego Images Detection Using Shallow...

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International Scientific-Practical conference Problems of Infocommunications Science and Technology

作者： Dmytro Progonov Institute of Physics and Technology Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9781665406833

Early detection of sensitive information leakage in communication systems is topical task today. This requires usage of advanced statistical processing methods for detection negligible changes of cover files, such as digital images, caused by message hiding. One of promising approaches for solving the task is learning an appropriate representation of cover and formed stego images that is sensitive to data embedding. This approach is widely used in modern stegdetectors based on utilization of convolutional neural networks. Achieving of high detection accuracy by stegdetector requires usage deep convolutional networks, whose computation-intensive re-train procedure limits fast adaptation to unknown embedding methods. For overcoming this limitation, we propose to use special types of neural networks, namely autoencoders that provides fast adaptation to changes of inputted data by preserving high restoration accuracy. The work is devoted to performance analysis of usage shallow denoising autoencoders for detection of stego images formed by advanced embedding methods. It is revealed that considered networks allows improving detection accuracy up to 1.5%-2% for the most difficult case of small cover image payload (less than 10%).

关键词： image coding Filtering Digital images Noise reduction Neural networks Performance analysis image restoration

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Improving the Efficiency of the Method of stochastic Gradient Identification of Objects in Binary and Grayscale images Using Their Preprocessing

Improving the Efficiency of the Method of Stochastic Gradien...

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Digital Signal processing and its Applications (DSPA), International conference on

作者： Magdeev Radik Gilfanovich Tashlinskii Alexander Grigorevich Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9781728175287

ISBN: (纸本)9781728175294

In this paper, we consider ways to improve the stochastic gradient method efficiency of object identification for binary and grayscale images using methods of image preprocessing. Identification of an object is understood as the recognition of an object on the image with its parameters estimation. Low-pass filtering and image equalization are considered as preliminary processing. The identification parameters convergence rate is investigated. The optimal sizes of Gaussian filter mask for binary and grayscale images were found based on COIL-20 images.

关键词： Gray-scale Object recognition Low pass filters Convergence Brightness stochastic processes

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SIDER: Single-image Neural Optimization for Facial Geometric Detail Recovery

SIDER: Single-Image Neural Optimization for Facial Geometric...

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International conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Aggelina Chatziagapi ShahRukh Athar Francesc Moreno-Noguer Dimitris Samaras Stony Brook University Institut de Robòtica i Informàtica Industrial CSIC-UPC

ISBN: (纸本)9781665426893

We present SIDER (Single-image neural optimization for facial geometric DEtail Recovery), a novel photometric optimization method that recovers detailed facial geometry from a single image in an unsupervised manner. Inspired by classical techniques of coarse-to-fine optimization and recent advances in implicit neural representations of 3D shape, SIDER combines a geometry prior based on statistical models and Signed Distance Functions (SDFs) to recover facial details from single images. First, it estimates a coarse geometry using a morphable model represented as an SDF. Next, it reconstructs facial geometry details by optimizing a photometric loss with respect to the ground truth image. In contrast to prior work, SIDER does not rely on any dataset priors and does not require additional supervision from multiple views, lighting changes or ground truth 3D shape. Extensive qualitative and quantitative evaluation demonstrates that our method achieves state-of-the-art on facial geometric detail recovery, using only a single in the-wild image.

关键词： Geometry Hair Solid modeling Three-dimensional displays Shape Optimization methods Lighting

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methods to enhance the automation of operational modal analysis 45

Methods to enhance the automation of operational modal analy...

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45th International conference on Vibroengineering

作者： Wiemann, Marcel Bonekemper, Lukas Kraemer, Peter Department of Mechanical Engineering University of Siegen Siegen Germany

The vibration-based damage detection and the monitoring of modal data are currently based on different Operational Modal Analysis (OMA) approaches. For the continuous monitoring of modal quantities, different techniques for automated feature extraction are known. Especially in recent years several research groups and companies have been working on the automatic interpretation of stability plots. Nevertheless, many questions regarding data pre-processing for OMA in time or frequency domain are still unanswered. The present paper deals with issues regarding effective pre-processing methods for OMA based on Covariance-stochastic Subspace Identification. In this context, the orthogonality of matrices after model order reduction, etc. are referred. This includes, for example, a comparison between the classical calculation of the reduced-order matrices and a procedure that preserves the orthogonality of these matrices. A method known from the signal denoising and image processing is also successful used to extract and select the modes. The mode extraction method is validated with an innovative three-dimensional stability plot. This paper does not claim to solve all tasks of an automated OMA, but it contributes the calculation of clean, easy to interpret, stability plots, which should facilitate the automatic evaluation in the future. The effectiveness of the algorithms is demonstrated by means of simulated (3DOF-StateSpace) and measured data of a laboratory structure described in [1]. Afterwards the results and the future works on the topic are discussed. Copyright © 2020 Marcel Wiemann, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Structural health monitoring

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stochastic Fusion for Multi-stream Neural Network in Video Classification

Stochastic Fusion for Multi-stream Neural Network in Video C...

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Annual Summit and conference of the Asia-Pacific-Signal-and-Information-processing-Association (APSIPA ASC)

作者： Huang, Yu-Min Tseng, Huan-Hsin Chien, Jen-Tzung Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu Taiwan Univ Michigan Dept Radiat Oncol Ann Arbor MI 48109 USA

ISBN: (纸本)9781728132488

Spatial image and optical how provide complementary information for video representation and classification. Traditional methods separately encode two stream signals and then fuse them at the end of streams. This paper presents a new multi-stream recurrent neural network where streams are tightly coupled at each time step. Importantly, we propose a stochastic fusion mechanism for multiple streams of video data based on the Gumbel samples to increase the prediction power. A stochastic backpropagation algorithm is implemented to carry out a multi-stream neural network with stochastic fusion based on a joint optimization of convolutional encoder and recurrent decoder. Experiments on UCF101 dalaset illustrate the merits of the proposed stochastic fusion in recurrent neural network in terms of interpretation and classification performance.

关键词： Recurrent neural networks

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Detection and Classification of Brain LesionDepending on statistical Features Textural Analysis 1

Detection and Classification of Brain LesionDepending on Sta...

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1st International conference on Pure Science, ISCPS 2020

作者： Mazher, Alaa N. Naeemah, Maysaa. R. Ali, Alyaa H. Computer Science Department University of Technology Baghdad Iraq Department of Physics College of Science for Women University of Baghdad Baghdad Iraq

The early detection of brain lesion which includes the stroke (Hemorrhage and Ischemic) and cancer helps the doctors to overcome the health problem in the future. The correct diagnose may save many people from death. the medical image processing including the supervised classification "support vector machine", "the gray level neighbors matrix", "Fisher's Discrimination Ratio(FDR)"and the Accuracy matrix to detect and classify the brain lesion, twenty twocomputed tomography (CT) brain images have been taken with size 512 512. This methods provides very good diagnosis of the brain lesion, the accuracy for cancer detection is 98%, for the Hemorrhage is 96%, for the ischemic 99% and normal 90%. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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A Fast Approach for the Log-Cumulants Method Applied to Intensity Sar image processing

A Fast Approach for the Log-Cumulants Method Applied to Inte...

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2020 IEEE Latin American GRSS and ISPRS Remote Sensing conference, LAGIRS 2020

作者： Rodrigues, F.A.A. Nobre, J.S. Vigaelis, R. Liesenberg, V. Marques, R.C.P. Medeiros, F.N.S. Federal University of Cariri Juazeiro do Norte Brazil Federal University of Ceara Department of Statistics and Applied Mathematics Fortaleza Brazil Federal University of Ceara Sobral Brazil Santa Catarina State University Santa Catarina Brazil Federal Institute of Education Science and Tecnology of Ceara Fortaleza Brazil DETI-Centro de Tecnologia Cx. Postal Teleinformatics Engineering Department Campus do Pici s/n Fortaleza CE6007 Brazil

ISBN: (纸本)9781728143507

Synthetic aperture radar (SAR) image processing and analysis rely on statistical modeling and parameter estimation of the probability density functions that characterize data. The method of log-cumulants (MoLC) is a reliable alternative for parameter estimation of SAR data models and image processing. However, numerical methods are usually applied to estimate parameters using MoLC, and it may lead to a high computational cost. Thus, MoLC may be unsuitable for real-time SAR imagery applications such as change detection and marine search and rescue, for example. Our paper introduces a fast approach to overcome this limitation of MoLC, focusing on parameter estimation of single-channel SAR data modeled by the GI0 distribution. Experiments with simulated and real SAR data demonstrate that our approach performs faster than MoLC, while the precision of the estimation is comparable with that of the original MoLC. We tested the fast approach with multitemporal data and applied the arithmetic-geometric distance to real SAR images for change detection on the ocean. The experiments showed that the fast MoLC outperformed the original estimation method with regard to the computational time. © 2020 IEEE.

关键词： Parameter estimation

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