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An adaptive enhancement method based on stochastic parallel gradient descent of glioma image

IET image processing 2023年第14期17卷 3976-3985页

作者： Wang, Hongfei Peng, Xinhao Ma, ShiQing Wang, Shuai Xu, Chuan Yang, Ping Chinese Acad Sci Key Lab Adapt Opt Chengdu Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China Univ Elect Sci & Technol China Sch Med Chengdu Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sichuan Acad Med Sci Dept Oncol Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Canc Inst Sichuan Acad Med Sci Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Dept Lab Med Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sichuan Prov Key Lab Human Dis Gene Study Chengdu Sichuan Peoples R China

Brain tumour diagnosis is significant for both physicians and patients, but the low contrast and the artefacts of MRI glioma images always affect the diagnostic accuracy. The existing mainstream image enhancement methods are insufficient in improving contrast and suppressing artefacts simultaneously. To enrich the field of glioma image enhancement, this research proposed a glioma image enhancement method based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. Firstly, this method modifies the cumulative distribution function on the image histogram and performs gamma correction on the image according to the modified histogram to obtain a contrast-enhanced image. Then, the method suppresses the artefacts of glioma images by total variational and wavelet denoising algorithm. To get better enhancement images, the optimal parameters in the proposed method are searched by the SPGD algorithm. The statistical studies performed on 580 real glioma images demonstrate that the authors' approach can outperform the existing mainstream image enhancement methods. The results show that the proposed method increases the discrete entropy of the image by 8.9% and the contrast by 2.8% compared to original images. The enhanced images are produced by the proposed method with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation. To enrich the field of glioma image enhancement, this paper proposed a glioma image enhancement framework based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. The results show that the proposed framework has superior performance compared to several existing glioma image enhancement methods. image

关键词： image denoising image enhancement parallel algorithms artefacts suppression contrast improvement histogram modification stochastic parallel gradient descent

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Brain tumour detection in magnetic resonance imaging using Levenberg-Marquardt backpropagation neural network

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IET image processing 2023年第1期17卷 88-103页

作者： Ghahramani, Marzieh Shiri, Nabiollah Islamic Azad Univ Shiraz Branch Dept Elect Engn Shiraz *** Iran

Magnetic resonance imaging (MRI) is a high-quality medical image that is used to detect brain tumours in a complex and time-consuming manner. In this study, a back propagation neural network (BPNN) along with the Levenberg-Marquardt algorithm (LMA) is proposed to classify MRIs and diagnose brain tumours in a simple and fast process. The BPNN has 10 neurons in the hidden layer, and the default function of the feedforward feeds is mean squared error (MSE). The LMA is optimized as a multivariable adaptive approach and considerably decreases the MSE of the BPNN, so the errors of the tumour classification are diminished. The proposed method follows four steps including preprocessing, skull removal, feature extraction, and classification. The input MRIs are converted to greyscale, resized, and thresholding is performed in the preprocessing step and followed by skull removal. Morphological operations of closing, opening, and dilation are used to segment abnormal areas in the MRIs, and the opening operator recognizes the tumour more accurately. Using statistical analysis and a grey-level co-occurrence matrix (GLCM) 12 features are extracted from the MRIs and used as the inputs of the BPNN. To evaluate the proposed method, 670 normal and 670 abnormal brain MRIs are used as input data, and the classification is performed in 0.494 s. The accuracy, sensitivity, specificity, precision, dice, recall, and MSE are 98.7%, 97.61%, 99.7%, 97.61%, 98.6%, 97.61%, and 0.005, respectively. The approach is accurate and fast for medical images classification.

关键词： medical image classification image segmentation feature extraction tumour classification default function Biology and medical computing input MRIs high-quality medical image image classification biomedical MRI Medical magnetic resonance imaging and spectroscopy multivariable adaptive approach brain tumour detection Biophysics of neurophysiological processes brain tumour diagnosis Neural nets backpropagation mean squared error opening operator greyscale Computer vision and image processing techniques Biomedical magnetic resonance imaging and spectroscopy statistical analysis grey-level cooccurrence matrix neural nets skull removal hidden layer preprocessing step image recognition MSE preprocessing removal Levenberg-Marquardt algorithm magnetic resonance imaging image thresholding abnormal brain MRIs BPNN morphological operations brain LMA Other topics in statistics Patient diagnostic methods and instrumentation tumours Levenberg-Marquardt backpropagation neural network medical image processing Probability theory, stochastic processes, and statistics

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Localized Binarization of Document images Based on Suprathreshold stochastic Resonance 4

Localized Binarization of Document Images Based on Suprathre...

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4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Yan, Xiaoyue Mu, Dazhong School of Information Engineering Beijing Institute Of Graphic Communication Beijing China

ISBN: (纸本)9798331528850

When processing text images with traditional binarization methods, the image background noise often causes the results to become blurred or leads to the loss of edge details. To solve this problem, this paper proposes an image binarization method based on stochastic resonance theory. First, we divide the image into sub-blocks and set a binarization threshold based on the statistical properties of the pixels in each sub-block. Next, the image signal is converted into a one-dimensional time series signal using Hilbert scanning. The processed signals are input into a threshold array system, which amplifies the weak edge information in the input signals through the stochastic resonance phenomenon. Subsequently, we performed modulation and inverse scanning on the output signals of the system to generate the binary image for each sub-block. Finally, all sub-block binary images were combined to complete the binarization of the overall image. Experimental results show that the method proposed in this paper can effectively retain the detailed information of document images and significantly outperforms the traditional binarization method regarding image quality. © 2024 IEEE.

关键词： stochastic systems

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Drone-towed electromagnetic and magnetic systems for subsurface characterization and archaeological prospecting

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NEAR SURFACE GEOPHYSICS 2024年第6期22卷 617-635页

作者： Vilhelmsen, Tobias B. Grayver, Alexander V. Dossing, Arne Tech Univ Denmark DTU Space Geomagnetisme & Geospace Centrifugevej 356 DK-2800 Kongens Lyngby Denmark Univ Cologne Inst Geophys & Meteorol Cologne Germany

Drone-based geophysical surveying is an emerging measuring platform. High time-cost efficiency and flexibility to survey over inaccessible areas make drones attractive or sometimes the only feasible option to carry geophysical measurements. This study presents a new drone-towed electromagnetic induction and magnetic gradient sensor system used for near-surface characterization and areal *** system uses various datasets to enhance processing and interpretation. The system includes;an electromagnetic induction instrument;magnetic sensors;GNSS-IMU system;photogrammetry;Lidar model data;and geoid model data. Robust data processing and stochastic inversion subsurface characterization for archaeological prospecting with drone-towed electromagnetic induction and magnetic gradient sensor systems. Robust statistical methods were used to process the *** conducted the fieldwork at one of the ancient Viking settlements in Denmark. The surveyed area was approximately 100x$\times$200 m. We then implemented and applied a one-dimensional laterally constrained non-linear stochastic inversion to image the subsurface electrical conductivity. The inversion results show a consistent conductive layer at 5-8 m depths, likely associated with the groundwater level. This conductive layer is disrupted under a prominent anomaly within a 2-4 m wide area. Our analysis showed that this conductivity disruption could be a flint mine extending 7 m deep. This anomaly also has a strong signature in magnetic gradient data.

关键词： airborne EM archaeogeophysics data processing magnetic modelling

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A statistical Approach to stochastic Computing Design and Analysis

A Statistical Approach to Stochastic Computing Design and An...

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作者： Baker, Timothy University of Michigan

学位级别：博士

stochastic computing (SC) is an unconventional computing style that uses probabilistic bitstreams to implement algorithms like those for machine learning, digital filtering, and image processing. SC's unusual encoding enables highly fault tolerant, low power, and small datapaths. For instance, a single AND gate performs multiplication in SC. SC's advantages make it attractive for use in small devices like wearables and biomedical implants, but widespread adoption of SC has been limited due to challenges including a steep accuracy-latency trade-off and expensive interface circuits. We address these challenges using a statistical approach. First, we develop a better framework for analyzing stochastic circuit errors and then use insights from statistical analysis to design and evaluate smaller and more accurate *** SC's many error sources is key to developing more accurate designs. However, existing approaches to accuracy analysis fail to provide a comprehensive account of all error types. The first part of this thesis proposes a novel framework named Bayesian Analysis of stochastic Errors (BASE). BASE is built on statistical estimation theory and identifies three key quantities that must be modeled or estimated to quantify a circuit's accuracy. BASE is used to better understand and address circuit errors and is compatible with prior methods for error analysis as well as new models that we propose in this thesis. Importantly, BASE reduces the reliance on black box simulation methods which provide limited insight and are prone to ***'s low-cost multipliers make it attractive for neural network and digital filtering applications. However, accurate addition is difficult to implement in SC and overhead from SC's data conversion circuits reduces SC's advantage for these applications. We address these problems by using insights from statistical modeling to develop new and better SC designs. Three such designs are introduced: 1) CeMux, a multiplexer

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Computational techniques for parameter estimation of gravitational wave signals

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 2022年第1期14卷 e1532-e1532页

作者： Meyer, Renate Edwards, Matthew C. Maturana-Russel, Patricio Christensen, Nelson Univ Auckland Dept Stat Auckland New Zealand Auckland Univ Technol Dept Math Sci Auckland New Zealand Univ Cote Azur Observ Cote Azur Artemis Nice France

Since the very first detection of gravitational waves from the coalescence of two black holes in 2015, Bayesian statistical methods have been routinely applied by LIGO and Virgo to extract the signal out of noisy interferometric measurements, obtain point estimates of the physical parameters responsible for producing the signal, and rigorously quantify their uncertainties. Different computational techniques have been devised depending on the source of the gravitational radiation and the gravitational waveform model used. Prominent sources of gravitational waves are binary black hole or neutron star mergers, the only objects that have been observed by detectors to date. But also gravitational waves from core-collapse supernovae, rapidly rotating neutron stars, and the stochastic gravitational-wave background are in the sensitivity band of the ground-based interferometers and expected to be observable in future observation runs. As nonlinearities of the complex waveforms and the high-dimensional parameter spaces preclude analytic evaluation of the posterior distribution, posterior inference for all these sources relies on computer-intensive simulation techniques such as Markov chain Monte Carlo methods. A review of state-of-the-art Bayesian statistical parameter estimation methods will be given for researchers in this cross-disciplinary area of gravitational wave data analysis. This article is categorized under: Applications of Computational Statistics > Signal and image processing and Coding statistical and Graphical methods of Data Analysis > Markov Chain Monte Carlo (MCMC) statistical Models > Time Series Models

关键词： Bayesian inference Markov chain Monte Carlo Nested Sampling parameter estimation signal processing

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The Lattice Overparametrization Paradigm for the Machine Learning of Lattice Operators 3rd

The Lattice Overparametrization Paradigm for the Machine Lea...

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3rd International Joint Conference on Discrete Geometry and Mathematical Morphology (DGM)

作者： Marcondes, Diego Barrera, Junior Univ Sao Paulo Inst Math & Stat Dept Comp Sci Sao Paulo Brazil Texas A&M Univ Dept Elect & Comp Engn College Stn TX USA

ISBN: (纸本)9783031577925;9783031577932

The machine learning of lattice operators has three possible bottlenecks. From a statistical standpoint, it is necessary to design a constrained class of operators based on prior information with low bias, and low complexity relative to the sample size. From a computational perspective, there should be an efficient algorithm to minimize an empirical error over the class. From an understanding point of view, the properties of the learned operator need to be derived, so its behavior can be theoretically understood. The statistical bottleneck can be overcome due to the rich literature about the representation of lattice operators, but there is no general learning algorithm for them. In this paper, we discuss a learning paradigm in which, by overparametrizing a class via elements in a lattice, an algorithm for minimizing functions in a lattice is applied to learn. We present the stochastic lattice descent algorithm as a general algorithm to learn on constrained classes of operators as long as a lattice overparametrization of it is fixed, and we discuss previous works which are proves of concept. Moreover, if there are algorithms to compute the basis of an operator from its overparametrization, then its properties can be deduced and the understanding bottleneck is also overcome. This learning paradigm has three properties that modern methods based on neural networks lack: control, transparency and interpretability. Nowadays, there is an increasing demand for methods with these characteristics, and we believe that mathematical morphology is in a unique position to supply them. The lattice overparametrization paradigm could be a missing piece for it to achieve its full potential within modern machine learning.

关键词： lattice overparametrization discrete morphological neural networks image processing mathematical morphology U-curve algorithms stochastic lattice descent

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A Robust stochastic image Segmentation Model for Medical images 19

A Robust Stochastic Image Segmentation Model for Medical Ima...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Messaouda, Larbi Abdelghani, Rouini Larbi, Samira University of Djelfa Department of Science and Technology Djelfa Algeria University of Djelfa Applied Automation and Industrial Diagnostic Laboratory Department of Science and Technology Djelfa Algeria University of Laghouat Department of Science and Technology Laghouat Algeria

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

image segmentation is one of the key problems in image processing. Among the different models and approaches developed, some of the commonly used statistical methods are based on the intensity homogeneity. In this paper, we present a novel robust stochastic method for medical image segmentation, which is able to treaty with intensity inhomogeneities in these type of images. The proposed method is based on the well-known Gaussian Probability Distribution. We designe a Partial Differential Equation (PDE)-based flow in order to achieve a maximum and best segmentation of the image. Herein, a complete comparative quantitative study of the considered approaches was established. Various criteria were calculated to evaluate the methods of segmentation. © 2022 IEEE.

关键词： image segmentation

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image Segmentation and Pre-processing for Lung Cancer Detection in Humans Based on Deep-Learning

Image Segmentation and Pre-Processing for Lung Cancer Detect...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Singh, Drishti Singh, Jaspreet School of Engineering and Sciences GD Goenka University Gurugram India

ISBN: (纸本)9798350333244

When it comes to cancer and its linked disorders, lung cancer is consistently ranked among the top causes of mortality. The primary method for making the diagnosis is to do a scan analysis of the patient's lungs. It's possible that an MRI, CT scan, or X-ray will be analyzed in this scan analysis. Due to the wide variety of imaging techniques that can be used to a patient's lungs, one of the challenging jobs that must be completed is the automated classification of lung cancer. methods involving machine learning, deep learning and image processing have demonstrated a significant amount of promise for the classification and identification of lung cancer. In this research, we demonstrate a successful strategy for detecting and classifying malignant and benign lung cancer-related CT scan pictures. This approach was developed as part of our research for this paper. The proposed method begins by classifying the photos after they have been processed with image processing techniques. After that, the supervised learning algorithms are utilized to further process the images. In this section, we have extracted statistical features as well as textural characteristics, and then we have fed several extracted features to multiple classifiers. We have utilized a total of seven distinct classifiers, which are referred to as the KNN classifier, SVM classifier, multinomial naive Bayes classifier, decision tree, SGD(stochastic gradient descent), MLP (multi-layer perceptron) and random forest. When training and testing these classifiers, we employed a dataset that included both benign and malignant lung cancer-related images. In the findings that were collected, it was discovered that the accuracy is highest which is approx. 88 percent(for MLP classifier), when compared to other classifiers. © 2023 IEEE.

关键词： image segmentation

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Strong-lensing source reconstruction with variationally optimized Gaussian processes

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2022年第1期512卷 661-685页

作者： Karchev, Konstantin Coogan, Adam Weniger, Christoph Univ Amsterdam Gravitat Astroparticle Phys Amsterdam GRAPPA Inst Theoret Phys Amsterdam Sci Pk 904 NL-1098 XH Amsterdam Netherlands Univ Amsterdam Delta Inst Theoret Phys Sci Pk 904 NL-1098 XH Amsterdam Netherlands SISSA Scuola Int Super Studi Avanzati Via Bonomea 265 I-34136 Trieste Italy

Strong-lensing images provide a wealth of information both about the magnified source and about the dark matter distribution in the lens. Precision analyses of these images can be used to constrain the nature of dark matter. However, this requires high-fidelity image reconstructions and careful treatment of the uncertainties of both lens mass distribution and source light, which are typically difficult to quantify. In anticipation of future high-resolution data sets, in this work we leverage a range of recent developments in machine learning to develop a new Bayesian strong-lensing image analysis pipeline. Its highlights are (a) a fast, GPU-enabled, end-to-end differentiable strong-lensing image simulator;(b) a new, statistically principled source model based on a computationally highly efficient approximation to Gaussian processes that also takes into account pixellation;and (c) a scalable variational inference framework that enables simultaneously deriving posteriors for tens of thousands of lens and source parameters and optimizing hyperparameters via stochastic gradient descent. Besides efficient and accurate parameter estimation and lens model uncertainty quantification, the main aim of the pipeline is the generation of training data for targeted simulation-based inference of dark matter substructure, which we will exploit in a companion paper.

关键词： gravitational lensing: strong methods: statistical techniques: image processing dark matter

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