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An entropy-based membership approach on type-ii fuzzy set (EMT2FCM) for biomedical image segmentation

ENGINEERING applications OF ARTIFICIAL INTELLIGENCE 2024年第PartA期127卷

作者： Bose, Ananya Maulik, Ujjwal Sarkar, Anasua St Thomas Coll Engn & Technol Dept Elect Engn Kolkata India Jadavpur Univ Dept Comp Sci & Engn Kolkata India

Medical image segmentation is a challenging task owing to its aliasing artifacts, existence of mixed pixels, interference of noise and blurring effect, etc. The brain and the spinal cord together constitute the Central Nervous System (CNS), with the brain being the most complex component. Brain segmentation is crucial for surgical planning, disease detection, and therapeutic interventions. A unique entropy based soft clustering approach (EMT2FCM) for the brain MRI imaging on both real and simulated datasets is proposed in this paper. This paper presents a refined version of the type-2 Fuzzy C-Means (FCM) technique (EMT2FCM) for isolating diverse tissues in Magnetic Resonance (MR) brain images, achieved through an enhanced entropy-based mem-bership function. Difficulty in edge detection occurs due to the vagueness caused by mixed pixels. To overcome the problem with mixed pixels a new membership function proposal has been given. EMT2FCM, i.e., fuzzy membership function with Shannon's entropy-based variation, is able to handle uncertainty and vagueness in images offered by mixed pixels, by implementing new distance criteria modification for the calculation of op-timum cluster. The approach has been validated using datasets from BRAINWEB (simulated) and IBSR (real-life). Following the deployment of our new algorithm, we assessed its performance using six observation criteria. The results demonstrated significant improvements in clustering performance, as evidenced by enhanced Mean Squared Error (MSE), Dice Index, and Jaccard Index, Accuracy, Sensitivity and Partition Indices validation measures. The evaluation of EMT2FCM in comparison to other state-of-the-art methods is established to reduce segmentation errors significantly.

关键词： Medical imaging Type-2 FCM Gaussian membership Entropy BRAINWEB IBSR Segmentation

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High dimensional Bio medical images denoising using wavelet transform and modified bilateral filter

High dimensional Bio medical images denoising using wavelet ...

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International conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Muhammad Usama Jabbar Ali Waqar Muhammad Awais Zohaib Mushtaq Muhammad Jamshed Abbass Muhammad Abdul Rehman Faisal Saleem Shehzad Ahmed Ahmed Zubair Jan Department of Electrical engineering National University of Sciences and Technology Islamabad Pakistan University degli Studi di Napoli Parthenope Italy Faculty of Engineering and Applied Science Riphah International University Faculty of Mechanical Engineering Wrocław University of Science and Technology

ISBN: (纸本)9781665460729

Biomedical images consist of salient information that helps in diagnosis of diseases. Significant amount of information is lost during process of transmission due to addition of noisy components especially for Computed Tomography (CT) scan, Ultrasound imaging and Magnetic Resonance Imaging (MRI). image denoising is an important and necessary pre-processing phase in image applications. Numerous techniques have been developed for denoising process over decades. The purpose is to remove noise from images. wavelet transform is current state of the art addition to analyze image denoising; able to achieve improved experimental results than from existing techniques. Generalized Gaussian distribution is considered to model noise. In this article, we introduce mix adaptive thresholding with modified bilateral filter. Simulation results demonstrate that attributes of images are improved. The Qualitative and quantitative assessment proves better denoised results for proposed method using parameters like Peak signal to Noise Ratio (PSNR), Mean Opinion Score (MOS) and Structural Similarity Index (SSIM) between the true and estimated biomedical images.

关键词： wavelet transforms Thresholding (Imaging) PSNR Ultrasonic imaging Computed tomography Magnetic resonance imaging Simulation

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Input-aware accuracy characterization for approximate circuits

Input-aware accuracy characterization for approximate circui...

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International conference on Dependable Systems and Networks Workshops (DSN-W)

作者： Ali Piri Salvatore Pappalardo Salvatore Barone Mario Barbareschi Bastien Deveautour Marcello Traiola Ian O’Connor Alberto Bosio ECL INSA Lyon CNRS UCBL CPE Lyon INL UMR5270 Univ Lyon Ecully France Dep. of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy Inria CNRS IRISA UMR6074 University of Rennes

It has been a while since Approximate Computing (AxC) is applied systematically at various abstraction levels to increase the efficiency of several applications such as image processing and machine learning. Despite its benefit, AxC is still agnostic concerning the specific workload (i.e., input data to be processed) of a given application. For instance, in signal processing applications (such as a filter), some inputs are constants (filter coefficients). Meaning that a further level of approximation can be introduced by considering the specific input distribution. This approach has been referred to as “input-aware approximation”. In this paper, we explore how the input-aware approximate design approach can become part of a systematic, generic, and automatic design flow by knowing the data distribution. In particular, we show how input distribution can affect the error characteristics of an approximate arithmetic circuit and also the advantage of considering the data distribution by designing an input-aware approximate multiplier specifically intended for a high-pass FIR filter, where the coefficients are constant. Experimental results show that we can significantly reduce power consumption while keeping an error rate lower than state-of-the-art approximate multipliers.

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Recognition of Hybrid Digital Watermarking Techniques using Digital image processing using Deep Learning Techniques

Recognition of Hybrid Digital Watermarking Techniques using ...

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Augmented Intelligence and Sustainable Systems (ICAISS), International conference on

作者： Chilumala Indira RM Noorullah Praveen Kumar Balguri Sowjanya Krishna Pinisetty Department of Computer Science and Engineering Institute of Aeronautical Engineerring Dundigal Hyderabad India Department of Aeronautical Engineering Institute of Aeronautical Engineerring Dundigal Hyderabad India Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China Chengdu China

Digital watermarking is the procedure to identify the source and creator of the data. This is a form of digital media marker such as video, audio or image format. It helps to protect the data from various external unauthorized access. This is done through embedded digital information in the carrier signal to identify ownership and copyright protection. Security and privacy concerns need to be improved due to the rapid increase in the transmission and receiving of digital data. Various sectors such as the navy, military, intelligence, and police are sending and receiving numerous data through online mode. The existing system utilizes Arnold map with QWT-DCT techniques which are unable to obtain invisibility in attack detection. To overcome the limitations of the existing system the proposed system is implemented. They are highly confidential and need to be secured and protected thus hybrid digital watermarking techniques such as nested forms of watermarking are employed. If the third parties tend to make any modification, then it is termed as an attack. The removal of digital watermarking by third parties through certain modifications are denoted through copyright protection application. For security applications and to avoid various types of attacks, Discrete wavelet Transform (DWT) and Non-sampled Contourlet Transform (NSCT) are employed. Security and privacy is obtained through the Quaternion Fourier Transform (QFT) based algorithm with human speech recognition. The pixel displacement with pixel encryption is enhanced to save the privacy of images. Digital watermarking requires security, robustness, watermarking keys and computational cost parameter. Thus to protect the data by neglecting various conventional attacks, digital watermarking techniques are implemented through deep convolutional neural networks.

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PROJECTED IMPROVED FISTA AND APPLICATION TO image DEBLURRING

PROJECTED IMPROVED FISTA AND APPLICATION TO IMAGE DEBLURRING

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IEEE International conference on image processing (ICIP)

作者： Pokala, Praveen Kumar Seelamantula, Chandra Sekhar Indian Inst Sci Dept Elect Engn Bangalore 12 Karnataka India

ISBN: (纸本)9781728163956

The analysis-sparse model has been shown to be efficient as compared to the synthesis-sparse model when the sparsifying transform is redundant particularly for image restoration applications. We pose the image deblurring problem as an optimization problem based on the analysis-sparse model considering the sparsifying basis to be a tight frame, more specifically, the shift-invariant discrete wavelet transform (SIDWT). We propose two algorithms, namely, projected improved fast iterative soft-thresholding algorithm (piFISTA) and projected improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (piFISTA-BN). The proposed algorithms are the analysis counterparts of the improved fast iterative soft-thresholding algorithm (iFISTA) and improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (iFISTA-BN), respectively, both of which consider the synthesis-sparse model. We demonstrate that piFISTA and piFISTA-BN significantly outperform FISTA, pFISTA, iFISTA, and iFISTA-BN considering standard objective metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). Further, we demonstrate empirically that the proposed algorithms converge faster than the state-of-the-art techniques.

关键词： Analysis sparse model tight frames projected FISTA improved FISTA beyond Nesterov's momentum

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Network Dynamics Based Multidomain Sensor Data processing and Fusion for Multidomain Operations 2

Network Dynamics Based Multidomain Sensor Data Processing an...

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conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations applications ii Part of SPIE Defense + Commercial Sensing conference

作者： Li, Bingcheng Lockheed Martin 1801 State Route 17CMD0315 Owego NY 13827 USA

ISBN: (纸本)9781510636040

Multidomain sensor data processing and fusion provide reliable ways for situational awareness in multidomain operations and receive great attention in both industry and academia. However, these data processing and fusion are complicated in implementation due to various modalities and high complexity of sensor data. In network dynamics, graph theory is used to represent complex data and extract information, and graph evolution is applied to analyze network dynamics. In this paper, combining the technologies of these two different domains, we propose using network dynamics to process and fuse multidomain sensor data for multidomain operations. First, we propose a graph-theory based framework for multidomain sensor data processing and fusion. Then we apply this general framework to multidomain sensor data processing. Using one-dimensional radio frequency (RF) signal processing, two-dimensional image processing and three-dimensional light detection and ranging (LIDAR) data analytics as examples, we demonstrate that with the proposed method, the same architecture can be used to extract critical features for these three types of sensor data. Furthermore, experiments also show that the proposed method creates higher performance than traditional methods.

关键词： Sensors Data processing signal processing Data fusion LIDAR 3D image processing image processing Clouds image sensors Diffusion

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Hyper Spectral image compression using Higher Order Orthogonal Iteration Tucker decomposition

Hyper Spectral Image compression using Higher Order Orthogon...

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IEEE India conference (INDICON)

作者： B. Sucharitha K. Anitha Sheela Electronics and Communication Engineering Jawaharlal Nehru Technological University Hyderabad India

ISBN: (纸本)9781665452724

Hyper spectral images need more memory to store, process, and transmit due to the large amount of information they carry. One of the most effective way to compress hyper spectral images is to represent them as a three-dimensional tensor. Tensors are multi-dimensional Structures. Tensors have a wide range of applications in numerical linear algebra, chemometrics, data mining, signal processing, statics, data mining, and machine learning. For dimensional reduction of tensors, many tensor decomposition methods have been developed, which we can adapt to Hyper spectral image compression. The proposed method uses Discrete wavelet Transform and Higher-Order Orthogonal Iteration Tucker Decomposition method to compress the Hyper spectral image. The simulation results were compared with 3 more tensor decomposition techniques. Four real hyper spectral images were used in the experimentation: Pavia University (610 X 340 X 103), Indian Pines adjusted (145 x 145 x 200), Salinas image (512 x 217 x 224), and Abu-beach (150 X 150 X 102). After processing, the perceptional quality of the Hyper spectral images is evaluated using PSNR and SSIM. When compared to the other three methods, the proposed method offers good PSNR and SSIM with High Compression.

关键词： Tensors image coding Three-dimensional displays Simulation Memory management Termination of employment Transforms

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Edge-AI on Wearable Devices: Myocardial Infarction Detection with Spectrogram and 1D-CNN

Edge-AI on Wearable Devices: Myocardial Infarction Detection...

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Mediterranean Electrotechnical conference (MELECON)

作者： Maria Gragnaniello Fiona Balbi Gabriella Martellotta Alessandro Borghese Vincenzo Romano Marrazzo Luca Maresca Giovanni Breglio Andrea Irace Michele Riccio Department of Electrical Engineering and Information Technology (DIETI) University of Naples Federico II Naples Italy

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

The Electrocardiogram (ECG) provides a detailed representation of the heart's electrical activity, emerging as a crucial resource for continuous cardiac health monitoring. Recent advances in Artificial Intelligence (AI) techniques have revolutionized ECG signal processing, creating new possibilities for every-day health monitoring. The exploitation of these AI technologies has driven a growing interest in wearable devices where the challenge is to implement these functionalities on limited memory resource hardware. In this scenario, the Edge Computing paradigm, where computation occurs near the data source rather than in a remote data center, emerges as a promising solution. This article proposes an efficient approach for Myocardial Infarction (MI) detection based on Deep Learning (DL) methods using spectrogram and a 1D Convolutional Neural Network (1D-CNN). The aim is to strike a balance between computational efficiency and accuracy enabling practical application on wearable devices. In the presented work, a study on the impact of the spectrogram parameters and results on the 1D-CNN was conducted. The final training phase yielded a remarkable accuracy of 95.94%, showcasing the efficacy of the proposed approach. Notably, the trained model was successfully deployed on a 32-bit microcontroller featuring an ARM Cortex-M4 architecture, underscoring the feasibility of real-world implementation for embedded systems in healthcare applications.

关键词： Training Accuracy image edge detection Electrocardiography Myocardium Real-time systems Wearable devices

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Veterinary image Enhancement using DWTDCT and Singular Value Decomposition 9

Veterinary Image Enhancement using DWTDCT and Singular Value...

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9th IEEE International conference on Communication and signal processing, ICCSP 2020

作者： Rajan, Bindhu K Harshan, Hiron M Venugopal, G. APJ Abdul Kalam Technological University NSS College of Engineering Palakkad Kerala India College of Veterinary and Animal Sciences Mannuthy Gynecology Department Thrissur Kerala India APJ Abdul Kalam Technological University NSS College of Engineering Instrumentation and Control Engineering Department KeralaPalakkad India

ISBN: (纸本)9781728149882

As imaging technology advances, one of the unavoidable part of image processing is the image enhancement. image enhancement is to improve the visual quality for specific applications. The proposed algorithm enhances the visual quality of the vaginoscopic images of bitches using Discrete wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). The input image is decomposed into four frequency bands by using DWT. The low frequency band of the transformed image is again transformed by DCT into low frequency DCT coefficients and higher DCT coefficients. New low frequency band is formed by SVD. Inverse Discrete Cosine Transform (IDCT) is applied to produce the equalized image. Inverse Discrete wavelet Transform (IDWT) of new low frequency band and the other three frequency band are combined to give the enhanced image. Different quality measures such as Root Mean Square Error (RMSE), Peak to signal Noise Ratio (PSNR), Entropy, mean and Standard deviation are used to evaluate the performance of the model. The proposed model is implemented using MATLAB 2017a and obtained an average RMSE of 1.645. PSNR, Entropy, mean and standard deviation values for the proposed model are 44.12dB, 6.57, 103.35, 0.11 respectively. This qualitative analysis shows that proposed method seems to be useful for enhancing veterinary images for further processing. © 2020 IEEE.

关键词： Singular value decomposition

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Theoretical Analysis of Quaternion image Matching Performance

Theoretical Analysis of Quaternion Image Matching Performanc...

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conference on Mobile Multimedia/image processing, Security, and applications

作者： DelMarco, Stephen P. BAE Syst 600 Dist Ave Burlington MA 01803 USA

ISBN: (纸本)9781510635760;9781510635753

Recently, quaternions have been applied to multi-channel image processing such as color image registration, edge detection, and image matching. The general approach consists of using up to four image components as separate components in a quaternion-valued image. Techniques from quaternion algebra and analysis, such as quaternion Fourier transforms and correlation, are exploited to implement the desired image processing operations. The motivation underlying use of quaternions involves the concept of holistic data processing. That is, information in separate channels is processed jointly, thereby leveraging correlations across channel. Furthermore, quaternion transforms, such as the Fourier and wavelet transforms, have been defined and can be applied to process quaternion-valued imagery. For example, analogues from classical signal processing, such as the convolution theorem, have been derived and can be used to reduce computational burden. Such quaternion-based image processing techniques can provide performance superior to classical approaches which process image components independently. However, under certain conditions, performance of quaternion-based methods fails to meet performance of independent channel processing methods. In this paper, we perform a theoretical analysis of the performance of quaternion image matching. We present conditions under which quaternion-based processing outperforms, or falls short of, independent channel processing methods. We produce numerical image matching results validating the theoretical performance analysis using synthetic data. We define and solve a linear system of equations to generate the synthetic data. Receiver Operating Characteristics curves are provided to demonstrate the improved performance of the quaternion image matching approach under the favorable conditions predicted by the theory.

关键词： hypercomplex quaternions correlation multi-channel image matching

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