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image Pre-processing for Differential Diagnosis of Multiple Sclerosis using Brain MRI 2

Image Pre-processing for Differential Diagnosis of Multiple ...

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2nd IEEE international conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Memon, Khuhed Yahya, Norashikin Yusoff, Mohd Zuki Hashim, Hilwati Ali, Syed Saad Azhar Siddiqui, Shahabuddin Universiti Teknologi PETRONAS Dept. of Electrical & Electronic Engineering Perak Seri Iskandar Malaysia Universiti Teknologi PETRONAS HiCoE-CISIR Dept. of Electrical & Electronic Engineering Perak Seri Iskandar Malaysia Universiti Teknologi MARA Faculty of Medicine Department of Radiology Selangor Sungai Buloh Malaysia Center for Smart Mobility & Logistics King Fahd University of Petroleum & Minerals Aerospace Engineering Dept. Dhahran Saudi Arabia Pakistan Institute of Medical Sciences Advanced International Hospital Department of Radiology Islamabad Pakistan

ISBN: (纸本)9798350347982

Multiple sclerosis (MS) is a type of auto-immune disease affecting the human brain. One of the challenges in clinical diagnosis of MS lies in its close similarity to another type of brain auto-immune disease known as neuromyelitis optica (MNO). Hence, various researchers are looking into the development of computer-aided differential diagnosis (CADD) of MS vs. MNO. Generally, the development of the CADD, involves image pre-processing, feature extraction, statistical analysis and training and testing of the classification model. The focus of this paper is on the 2 critical pre-processing steps prior to classification of MS vs. MNO, namely skull stripping and lesion segmentation. In particular, both skull stripping and brain lesion segmentation are tested with a popular semantic segmentation technique, DeepLabV3. For comparison, a recently published technique, SynthStrip from FreeSurfer [1], developed by Harvard University is also tested for brain extraction / skull removal. Similarly for segmentation of brain lesions, Lesion Segmentation Tool in SPM Toolbox is tested and compared. The results from this study provides insight on what are the appropriate pre-processing techniques for the development of an reliable automatic differential diagnosis tool for multiple sclerosis conditions. © 2023 IEEE.

关键词： Computer aided diagnosis

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image Super-resolution Reconstruction Based on Multi-layer Parallel Residual Network 2

Image Super-resolution Reconstruction Based on Multi-layer P...

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2nd international conference on Computers, Information processing and advanced Education, CIPAE 2021

作者： Wang, Huifeng Xu, Yan Wei, Yiming Lanzhou Jiaotong University School of Electronic and Information Engineering Gansu China

ISBN: (纸本)9781665426657

The existing methods can improve the overall visual effect of the image, but the reconstructed image is still lack of high-frequency information, leading to texture blur. To solve these problems, this paper proposes an image super-resolution reconstruction algorithm based on multi-layer parallel convolution and residual network. Taking the multilayer parallel structure as the whole framework, firstly, different convolution combinations are used to enrich the feature information of the parallel structure, and the jump connection is added to further enrich the feature and fuse the output to extract more high-frequency information. Secondly, an adaptive residual network is introduced to supplement information and optimize network performance. Finally, perceptual loss is used to improve the overall quality of the restored image. Experimental results show that the proposed algorithm performs well for the reconstructed image, and the PSNR and structural similarity are significantly improved in the objective evaluation. © 2021 IEEE.

关键词： Adaptive systems Convolution Superresolution Reconstruction algorithms Feature extraction Visual effects Nonhomogeneous media

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

3rd International Conference on Machine Intelligence and Sig...

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

ISBN: (纸本)9789811908392

The proceedings contain 67 papers. The special focus in this conference is on Machine Intelligence and signal processing. The topics include: Internet of Things: A Survey on Fused Machine Learning-Based Intrusion Detection Approaches;image Fusion-Based Watermarking in IWT-SVD Domain;on Twin Bounded Support Vector Machine with Pinball Loss;traffic Rule Violation Detection System: Deep Learning Approach;template-Based Thinning Method for Handwritten Gujarati Character’s Strokes and its Classification for Writer-Dependent Gujarati Font Synthesis;a Model on Intrusion Detection Using Firefly Algorithm and Classical Machine Learning;deep Learning Framework Based on Audio–Visual Features for Video Summarization;a Comparative Study of Deep Learning Models for Word-Sense Disambiguation;MultiYOLO: Learning New YOLO Categories Without Full Retraining;Analysis of Fifteen Approaches to Automated COVID-19 Detection Using Radiography images;vehicle Routing Problem Using Reinforcement Learning: Recent Advancements;disease Detection of Plant Leaves with the Aid of Region Growing and Neural Network: A Comparative Analysis;a Web Application for Early Prediction of Diabetes Using Artificial Neural Network: A Comparative Study;machine Learning Equipped Web-Based Disease Prediction and Recommender System;comparing the Predictive Accuracy of Machine Learning algorithms for Neonatal Mortality Risk Classification;Extraction of Waterbody Using Object-Based image Analysis and XGBoost;Deep Learning-Based image Retrieval in the JPEG Compressed Domain;fake News Detection Using Genetic Algorithm-Based Feature Selection and Ensemble Learning;Application of SPSS for Forecasting of Renewable Energy as Future Energy in India;robust Multi-task Least Squares Twin Support Vector Machines for Classification;human Emotion Classification Based on Speech Enhancement Using Neural Networks.

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Medical image Watermarking Scheme in Transform Domain based on Asymmetric crypto-system and Arnold Chaotic Map 44

Medical Image Watermarking Scheme in Transform Domain based ...

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44th international conference on Telecommunications and signal processing (TSP)

作者： Nadhir, Nouioua Ali, Seddiki Abdelkrim, Ghaz Univ Djillali Liabes Sidi Bel Abbes Telecommun & Digital Signal Proc Lab Sidi Bel Abbes Algeria Univ Djillali Liabes Sidi Bel Abbes Commun Networks Architectures & Multimedia Lab Sidi Bel Abbes Algeria

ISBN: (纸本)9781665429337

Sharing a secret via the internet needs huge protection challenges to keep it in clean hands, and far away from several ways of maleficent manipulations, e.g., hacking and spying. Nowadays, experts proposed many encryption algorithms that work in spatial domain as well as frequency domain, to provide certain amount of privacy for this public network navigators. The proposed algorithm is about embedding an encrypted Electronic Patient Record (EPR) in Discrete Cosine Transform (DCT) resultant of a medical image that is decomposed into third level with Discrete Wavelet Transform (DWT), where the EPR is encrypted using Elgamal encryption, and Arnold map is also exerted in order to increase the randomness and unpredictability levels and to make the algorithm more complex. A variety of tests has been performed to prove the proposed scheme robustness.

关键词： Arnold Chaotic Map Discrete Cosine Transform Discrete Wavelet Transform Elgamal Crypto-System Medical images Digital Watermarking

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Compressive Sensing Magnetic Resonance image Reconstruction and Denoising using Convolutional Neural Network 1

Compressive Sensing Magnetic Resonance Image Reconstruction ...

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1st international conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021

作者： Singh, Ram Kaur, Lakhwinder Department of Computer Science and Engineering Punjabi University Patiala India

Restoration of high-quality brain Magnetic Resonance image (MRI) from the sparse under-sampled complex k-space signal is a widely studied ill-posed inverse transform problem. A deep learning-based data-adaptive and data-driven convolutional technique has been proposed for high-quality MRI recovery from its under-sampled complex domain k-space signal. The uniform subsampling process is very slow in phase-encoding to generate high-resolution images. The longer scan times degrade the perceptual image quality. Various factors contribute to image degradation during data acquisition such as the inception of body motion artifacts, the thermal energy effects of the body, and random noise artifacts due to voltage fluctuations. Keeping in view the patient's critical condition and comfort, longer scan times are not preferred in practice. To reduce the image acquisition time, noise levels, and motion artifacts in the MR images, Compressive Sensing (CS) provides an accelerated way to reconstructs the high-quality MR image from very limited signal measurements acquired much below the Nyquist rate. However, such data acquisition strategies require advanced computer algorithms for the reconstruction of high-quality MRI from the undersampled MRI data. An improved CNN-based MRI reconstructed algorithm has been presented in this paper which shows better performance to reconstruct high-quality MRI than similar other MR image reconstruction algorithms. The performance of the proposed algorithm is measured by image quality checking tools such as normalized-MSE, PSNR, and SSIM. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： image reconstruction

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DECOMPOSING TEXTURES USING EXPONENTIAL ANALYSIS

DECOMPOSING TEXTURES USING EXPONENTIAL ANALYSIS

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IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Hou, Yuan Cuyt, Annie Lee, Wen-shin Bhowmik, Deepayan Univ Antwerp Dept Comp Sci B-2020 Antwerp Belgium Univ Stirling Div Comp Sci & Math Stirling FK9 4LA Scotland Shenzhen Univ Coll Math & Stat Shenzhen 518060 Guangdong Peoples R China

ISBN: (纸本)9781728176055

Decomposition is integral to most image processing algorithms and often required in texture analysis. We present a new approach using a recent 2-dimensional exponential analysis technique. Exponential analysis offers the advantage of sparsity in the model and continuity in the parameters. This results in a much more compact representation of textures when compared to traditional Fourier or wavelet transform techniques. Our experiments include synthetic as well as real texture images from standard benchmark datasets. The results outperform FFT in representing texture patterns with significantly fewer terms while retaining RMSE values after reconstruction. The underlying periodic complex exponential model works best for texture patterns that are homogeneous. We demonstrate the usefulness of the method in two common vision processing application examples, namely texture classification and defect detection.

关键词： Exponential analysis multivariate image decomposition texture analysis

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SEMASUPERPIXEL: A MULTI-CHANNEL PROBABILITY-DRIVEN SUPERPIXEL SEGMENTATION METHOD

SEMASUPERPIXEL: A MULTI-CHANNEL PROBABILITY-DRIVEN SUPERPIXE...

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IEEE international conference on image processing (ICIP)

作者： Wang, Xuehui Zhao, Qingyun Fan, Lei Zhao, Yuzhi Wang, Tiantian Yan, Qiong Chen, Long Sun Yat Sen Univ Guangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Northwestern Univ Evanston IL 60208 USA City Univ Hong Kong Hong Kong Peoples R China SenseTime Res Beijing Peoples R China

ISBN: (纸本)9781665441155

Superpixel, an efficient image segmentation approach, aggregates a group of similar pixels into the same cluster. Existing superpixel algorithms still mainly focus on the color information while ignoring the semantic distribution knowledge. In this paper, we propose a semantic information-driven method that adopts multi-channel semantic probabilities for superpixel segmentation. By conducting statistical analysis on the semantic output and then formulating the distance measure, the prior knowledge of the semantic with a dynamic confidence value could be utilized by our method during the global update effectively. Extensive experimental evaluations show that our method achieves a leading segmentation quality and convergence speed, compared to other five state-of-the-art algorithms, as measured by boundary recall, undersegmentation error, and explained variation.

关键词： Superpixel segmentation image segmentation image processing

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Can Existing image Quality Metrics Assess Enhanced images? 4

Can Existing Image Quality Metrics Assess Enhanced Images?

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4th international conference on Information Communication and signal processing, ICICSP 2021

作者： Huang, Gehang Liu, Xinwei HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (纸本)9781665407571

Most of image quality metrics are designed for degraded images, and their performance are highly correlated with the human visual system. The rapid development of image enhancement techniques raises a new issue: how to evaluate the performance of image enhancement methods? It is obvious that many benefits can be obtained if existing image quality metrics can be applied to the performance evaluation of image enhancement algorithms. However, quite a few studies have investigated such an issue. In this paper, we conduct psychophysical experiments to investigate the performance of existing image quality metrics on enhanced images. Two databases that specifically designed for enhanced images are selected in the experiments for the collection of subjective evaluation results. The objective evaluation results are obtained from six selected image quality metrics. By calculating the correlation between subjective and objective results we can answer the question: the performance of existing selected image quality metrics is low on enhanced images. © 2021 IEEE.

关键词： image enhancement

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Lightweight image Dehazing Neural Network Model Based on Estimating Medium Transmission Map by Intensity 19th

Lightweight Image Dehazing Neural Network Model Based on Est...

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19th Pacific Rim international conference on Artificial Intelligence (PRICAI)

作者： Jin, Tian-Hu Tao, Yan-Yun Guo, Jia-Ren Huang, Zi-Hao Zheng, Jian-Yin Soochow Univ Sch Rail Transportat Suzhou 215000 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9783031208676;9783031208683

Single image dehazing is a challenging ill-posed problem. The key to achieve haze removal is to estimate an accurate medium transmission map. By redefining the atmospheric scattering model, we obtain a new transmittance map scattering model, haze image and haze-free image, derive the medium transmission as a function of the scene intensity only, also deduce a priori condition that the intensity of hazy image is higher than that of haze-free image, and propose a lightweight image dehazing neural network (Intensity neural network, I-Net) based on estimating medium transmission map by intensity. I-Net uses a convolutional neural network (CNN) as the backbone, and takes the intensity of hazy image as the input, and outputs the intensity of haze-free image, the medium transmission map and the original haze-free image, also joints the priori condition derived previously to obtain a more accurate medium transmittance map. In this paper, the dehazing algorithm obtains the intensity of haze-free image through I-Net, derives the transmittance map using the functional relationship between transmittance and scene intensity, and finally recovers the original haze-free image through the transmittance map scattering model. The experimental results show that our dehazing results are clearer and more natural. The subjective and objective evaluations show that our image dehazing algorithm can achieve better dehazing results than traditional algorithms, and outperforms current advanced algorithms in terms of Peak signal to Noise Rate (PSNR) and Structure Similarity Index Measurement (SSIM).

关键词： Single image dehazing Atmospheric scattering model Medium transmission map Deep CNN

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ADVMIX: DATA AUGMENTATION FOR ACCURATE SCENE TEXT SPOTTING

ADVMIX: DATA AUGMENTATION FOR ACCURATE SCENE TEXT SPOTTING

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IEEE international conference on image processing (ICIP)

作者： Huang, Yizhang Fang, Kun Huang, Xiaolin Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China

ISBN: (纸本)9781665441155

Accurate scene text spotting models ask for effective data augmentation algorithms. This paper presents a novel data augmentation algorithm called AdvMix that integrates techniques of adversarial attack and mixup. First, it utilizes the PGD method to synthesize adversarial samples. Second, it is the first work to implement feature-wise mixup between original data and the associated adversarial sample to enhance data augmentation. AdvMix has been evaluated over ICDAR2013 and ICDAR2015 datasets, showing its superior performance in improving accuracy of scene text spotting models.

关键词： Scene Text Spotting Data Augmentation AdvMix Adversarial Attack

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