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Automated and Online Eye Blink Artifact Removal from Electroencephalogram

Automated and Online Eye Blink Artifact Removal from Electro...

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IEEE International conference on signal and image processing applications (IEEE ICSIPA)

作者： Egambaram, Ashvaany Badruddin, Nasreen Asirvadam, Vijanth S. Fauvet, Eric Stolz, Christophe Begum, Tahamina Univ Teknol PETRONAS Dept Elect & Elect Engn Ctr Intelligent Signal & Imaging Res Seri Iskandar Perak Malaysia Univ Bourgogne CNRS 6000 VIBOT Lab Le2iERL Dijon France Univ Sains Malaysia Dept Neurosci George Town Kelantan Malaysia

ISBN: (纸本)9781728133775

Eyeblink artifacts often contaminates electroencephalogram (EEG) signals, which could potentially confound EEG's interpretation. A lot offline methods are available to remove this artifact, but an online solution is required to remove eyeblink artifacts in near real time for EEG signal to be beneficial in applications such as brain computer interface, (BCI). In this work, approaches that combines unsupervised eyeblink artifact detection with Empirical Mode Decomposition (EMD) and Canonical Correlation Analysis (CCA) are proposed to automatically identify eyeblink artifacts and remove them in an online setting. The proposed approaches are analysed and evaluated in terms of artifact removal accuracy and ability of the approaches to retain neural information in an EEG signal. Analysis has discovered that the approaches have achieved more than 98% accuracy in detecting and removing eyeblink artifacts in real time. The approaches have produced very low reconstruction error as well, the least is 0.148 in average. These algorithms took about 12ms in average to clean a is length of EEG segment, which is fast enough to process the signals in real time.

关键词： Electroencephalography

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BiT_C Detector: Gallstones Detection for Ultrasound images

BiT_C Detector: Gallstones Detection for Ultrasound Images

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IEEE International conference on signal and image processing applications (IEEE ICSIPA)

作者： Sari, Suhaila Zainuddin, Nadzirah Mohd, Mohd Norzali Taujuddin, Nik Shahidah Afifi Mohd Roslan, Hazli Univ Tun Hussein Onn Malaysia Fac Elect & Elect Engn Johor Baharu Malaysia Univ Tun Hussein Onn Malaysia Fac Engn Technol Johor Baharu Malaysia

ISBN: (纸本)9781728133775

Ultrasound images are captured in real-time. They can present the anatomy and activities of the body's internal organs. Ultrasound is useful to observe changes in the form, size or shape of organs, tissues, and vessels. It is also utilized to detect abnormal masses, such as tumors or cancer cells. Ultrasound imaging techniques can also be used to detect gallstones, hard particles that develop in the gallbladder. Ultrasound-based diagnosis may accurately detect gallstones as small as 2 mm in diameter. However, there are difficulties when ultrasound image is low in brightness or consist noise. Therefore, accurate gallstones detector by using only ultrasound image is still a challenging task. The existing edge detection techniques are basically sensitive to noise. There are two types of image will be tested to determine the result which is ultrasound images with gallstones and ultrasound images without gallstones. The BiT_C Detector intends to reduce the noises that available on each test image to facilitate the next threshold process. The threshold will be set to several values from 0.1 to 0.9 to determine the optimal threshold value for each image. There is also five comparisons to experimenting the performance of the BiT_C Detector and four other conventional methods. From the results, it can be proven that BiT_C Detector can detect gallstones ultrasound images.

关键词： Bilateral filter Canny edge detection gallstones threshold ultrasound image

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Ultrasound Tomography Hardware System for Multiphase Flow Imaging

Ultrasound Tomography Hardware System for Multiphase Flow Im...

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IEEE International conference on signal and image processing applications (IEEE ICSIPA)

作者： Zulkiflli, N. A. Pusppanathan, J. Phang, F. A. Ibrahim, S. Rahim, R. A. Nawi, N. D. Rahiman, M. H. F. Tee, K. S. Ayob, N. M. N. Univ Teknol Malaysia Sch Biomed Engn & Hlth Sci Skudai Johor 81310 Malaysia Univ Teknol Malaysia Sports Innovat Technol Ctr SITC Skudai 81310 Johor Malaysia Univ Teknol Malaysia Ctr Engn Educ CEE Skudai Johor 81310 Malaysia Univ Teknol Malaysia Sch Elect Engn Control & Mechatron Engn Skudai 81310 Johor Malaysia Univ Tun Hussein Onn Malaysia Dept Mech & Robot Engn JER Parit Raja 86400 Johor Malaysia Univ Teknol Malaysia Sch Educ Fac Social Sci & Humanities Skudai 81310 Johor Malaysia Univ Malaysia Perlis Sch Mechatron Engn Kangar 01000 Perlis Malaysia Emcee Technol Skudai 81310 Johor Malaysia

ISBN: (纸本)9781728133775

This paper presents the development of an ultrasonic tomography system for multiphase flow imaging. Ultrasonic tomography (UT) modality has a hard-field sensing principle which makes feasible to measure liquid-gas, liquidliquid and liquid-solid flow composition within a pipe vessel. The sensor configuration for the UT system is discussed along with its signal conditioning circuitry and image reconstruction technique. The feasibility of the system is experimentally carried out on a 100 mm acrylic pipe for a stratified multiphase flow measurement to reconstruct tomographic images.

关键词： ultrasonic tomography signal conditioning image reconstruction multiphase flow imaging

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Compressed Sensing and Generative Models 18

Compressed Sensing and Generative Models

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conference on wavelets and Sparsity xVIII

作者： Price, Eric UT Austin Austin TX 78712 USA

ISBN: (数字)9781510629707

ISBN: (纸本)9781510629707

The goal of compressed sensing is make use of image structure to estimate an image from a small number of linear measurements. The structure is typically represented by sparsity in a well-chosen basis. We describe how to achieve guarantees similar to standard compressed sensing but without employing sparsity at all {instead, we suppose that vectors lie near the range of a generative model G: R-k -> R-n. Our main theorem here is that, if G is L-Lipschitz, then roughly O(k log L) random Gaussian measurements suffice;this is O(kd log n) for typical d-layer neural networks. The above result describes how to use a model to recover a signal from noisy data. But if the data is noisy, how can we learn the generative model in the first place? This paper will describe how to incorporate the measurement process in generative adversarial network (GAN) training. Even if the noisy data does not uniquely identify the non-noisy signal, the distribution of noisy data may still uniquely identify the distribution of non-noisy signals. In presenting the above results we summarize and synthesize the work of [BJPD17] and [BPD18]. We then add some observations on the limitations of the approaches.

关键词： Compressed sensing Data modeling Neural networks image processing

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Systolic Murmurs Diagnosis Improvement by Feature Fusion and Decision Fusion

Systolic Murmurs Diagnosis Improvement by Feature Fusion and...

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IEEE International conference on signal and image processing applications (IEEE ICSIPA)

作者： Akbari, Somayeh Ghassemian, Hassan Akbari, Zahra Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9781728133775

Acoustic sound generated by the heart mechanical activity, can provide useful information about the condition of heart valves. The heart sound auscultation is the fundamental tool in the evaluation of the cardiovascular system. The advantage of this method is fast, inexpensive and noninvasive. Due to human auscultatory limitation and non-stationary characteristics of phonocardiogram signals (PCG), diagnosis based on sounds that are heard via a stethoscope is difficult skill, therefor it requires a lot of practice. This study has proposed a biomedical automatic system for classification of PCG signals, which, recorded by a digital stethoscope. In order to extract various characteristics of PCG signals, the power spectrum estimation, wavelet transform (WT) and Mel frequency Cepstrum coefficients (MFCC) have been used in feature extraction step. Features are given to four classifiers: support vector machine (SVM), k-nearest neighbor (k-NN), multilayer perceptron (MLP) and maximum likelihood (ML). The majority voting combination rule is utilized for fusion of different classifiers. The proposed method has been examined on dataset of 90 PCG records containing healthy and three types of cardiac valve diseases (pulmonary stenosis (PS), Atrial Septal Defect (ASD) and Ventricular Septal Defect (VSD)). The experimental results demonstrate that the classifier fusion rule significantly increases the diagnostic accuracy of abnormal PCG. Our proposed method can be used for online classification of PCG in intelligent diagnosis systems.

关键词： PCG signal MFCC algorithm power spectrum estimation wavelet transform feature fusion decision fusion

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Recovery of Undersampled Sparse signals using Combined Smoothed l0 - l1 Norm

Recovery of Undersampled Sparse Signals using Combined Smoot...

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IEEE International conference on signal and image processing applications (IEEE ICSIPA)

作者： Haider, Hassaan Shah, Jawad Ali Kadir, Kushsairy Nasir, Haidawati Int Islamic Univ Fac Engn & Technol Dept Elect Engn Islamabad Pakistan Univ Kuala Lumpur British Malaysian Inst Elect Technol Sect Kuala Lumpur Malaysia Univ Kuala Lumpur Malaysian Inst Informat Technol Comp Engn Sect Kuala Lumpur Malaysia

ISBN: (纸本)9781728133775

Compressed Sensing is a modern sampling technique that caught the attention of the scientific community due to its diverse applications. It accurately recovers sparse signals/images from fewer samples that are well below the Nyquist Criterion. The linear recovery of under-sampled sparse signals/images is not possible using l(2)-norm minimization. The l(2) norm based least squares linear recovery minimizes the total energy of the error resulting in a nonsparse solution. It is a proven fact that non-linear l(1)-norm minimization promotes sparsity in the solution, which is desired in sparse signal recovery, however, it increases the computational complexity of the algorithm. The principal method for sparse signals reconstruction is based on l(0)-norm minimization that looks for a feasible solution having a minimum number of non-zeros and finding the solution to the l(0)-norm minimization is a combinatorial problem. There are non-linear reconstruction techniques based on smoothed l(0) and l(1) norm approximations are proposed in the literature that are proven to be computationally efficient. In this research paper, the novel technique for recovery of a sparse signal is proposed, where l(0) and l(1) norm are combined to accurately recover compressively sampled sparse signals. The experimental results have shown that the proposed technique is more efficient than traditional smooth approximations of l(0) and l(1) norm.

关键词： Sparse signal recovery Compressed sensing smoothed l(0) norm smoothed l(1) norm

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A New Real-Time Embedded Video Denoising Algorithm 13

A New Real-Time Embedded Video Denoising Algorithm

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conference on Design and Architectures for signal and image processing (DASIP)

作者： Petreto, Andrea Romera, Thomas Lemaitre, Florian Masliah, Ian Gaillard, Boris Bouyer, Manuel Meunier, Quentin L. Lacassagne, Lionel Sorbonne Univ CNRS LIP6 F-75005 Paris France Lheritier Alcen F-95862 Cergy Pontoise France

ISBN: (纸本)9781728140742

Many embedded applications rely on video processing or on video visualization. Noisy video is thus a major issue for such applications. However, video denoising requires a lot of computational effort and most of the state-of-the-art algorithms cannot be run in real-time at camera framerate. This article introduces a new real-time video denoising algorithm for embedded platforms called RTE-VD. We first compare its denoising capabilities with other online and offline algorithms. We show that RTE-VD can achieve real-time performance (25 frames per second) for qHD video (960x540 pixels) on embedded CPUs and the output image quality is comparable to state-of-the-art algorithms. In order to reach real-time denoising, we applied several high-level transforms and optimizations (SIMDization, multi-core parallelization, operator fusion and pipelining). We study the relation between computation time and power consumption on several embedded CPUs and show that it is possible to determine different frequency and core configurations in order to minimize either the computation time or the energy.

关键词： Embedded system Video denoising Real-time image processing night-vision SIMD

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Quantitative Evaluation of Four-Dimensional versus Three- Dimensional Reconstruction on xCAT Phantom Under Different Sampling Rates 3

Quantitative Evaluation of Four-Dimensional versus Three- Di...

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3rd International conference on Communications, signal processing, and their applications (ICCSPA)

作者： Al-Momani, Sajedah Elabed, Salma Dhou, Salam Amer Univ Sharjah Biomed Engn Grad Program Sharjah U Arab Emirates Amer Univ Sharjah Dept Comp Sci & Engn Biomed Engn Grad Program Biosci & Bioengn Res Inst Sharjah U Arab Emirates

ISBN: (纸本)9781728120850

Precise targeting of tumors is essential in radiotherapy. Imaging the lungs, however, is correlated with motion artifacts due to respiration. Motion artifacts influence achieving the required image quality and can cause uncertainty in target precision and dose calculations. Respiratory-correlated four-dimensional cone beam computed tomography ( 4D-CBCT) is an imaging technique proposed to resolve motion artifacts. However, the scans generated from this method are associated with streaking artifacts due to the limited number of projections binned within different respiratory phases. The main objective of this study is to provide a systematic comparison and evaluation of the quality of 4D images produced at several sampling rates. The evaluation is done in terms of the streaking artifacts, signal-to-noise ratio, and sharpness of edges in structures affected by respiratory motion. Experimental results showed that increasing the number of projections used in reconstructing 4D images improve image quality.

关键词： Cone-Beam Computed Tomography 4D image Reconstruction

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Comparison of Super Resolution Methods in Magnetic Resonance images for Small Animals

Comparison of Super Resolution Methods in Magnetic Resonance...

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IEEE International conference on signal and image processing applications (IEEE ICSIPA)

作者： Than, Joel C. M. Noor, Norliza Mohd Rijal, Omar M. Mustapha, Muzaimi Norazit, Anwar Mehat, Muhammad Zulfadli Univ Teknol Malaysia Razak Fac Technol & Informat Kuala Lumpur Malaysia Univ Malaya Inst Math Sci Kuala Lumpur Malaysia Univ Sains Malaysia Dept Neurosci Kubang Kerian Kelantan Malaysia Univ Malaya Fac Med Dept Biomed Sci Kuala Lumpur Malaysia Univ Putra Malaysia Fac Med & Hlth Sci Serdang Selangor Malaysia

ISBN: (纸本)9781728133775

Super resolution (SR) is an array of methods that utilize different approaches to enhance the quality and resolution of an image. This is particularly useful for images that are very small and have low quality. Small images are usually obtained due to the limitation of imaging capture systems or the subject captured is small. For small animals such as rats, imaging can be difficult and expensive to produce high-resolution images. Therefore, SR is a very relevant field of study for small animals. The study of small animals involve many impactful fields such as testing of harmful chemicals on the biological processes. The objective of this study is to compare 11 SR methods for rat magnetic resonance images (MRI). This study is a pilot study and the beginning of the research to see the effect of kratom that is a hallucinogen misused in south east Asia. This study used chosen images from six rats. These MRI images were captured at UM MRI Research Centre. This study compared the quality of SR methods using several measures including Peak Sound to Noise Ratio (PSNR), Sound to Noise Ratio (SNR), Mean Square Error (MSE), Structural Similarity Index (SSI). This study compared these methods on two different size factors of resolution which were two and four. The results show promising results for the next stage of research.

关键词： MRI resolution small enhancement image

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image-based cell sorting using artificial intelligence 5

Image-based cell sorting using artificial intelligence

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conference on High-Speed Biomedical Imaging and Spectroscopy V held at SPIE BiOS conference

作者： Herbig, Maik Nawaz, Ahmad Ahsan Urbanska, Marta Noetzel, Martin Kraeter, Martin Rosendahl, Philipp Herold, Christoph Toepfner, Nicole Kubankova, Marketa Goswami, Ruchi Abuhattum, Shada Reichel, Felix Mueller, Paul Taubenberger, Anna Girardo, Salvatore Jacobi, Angela Guck, Jochen Tech Univ Dresden Biotechnol Ctr Dresden Germany Max Planck Inst Sci Light Erlangen Germany Max Planck Zentrum Phys & Med Erlangen Germany Zellmech Dresden GmbH Dresden Germany Tech Univ Dresden Univ Klinikum Carl Gustav Carus Klin & Poliklin Kinder & Jugendmed Dresden Germany Tech Univ Dresden Ctr Mol & Cellular Bioengn Ctr Regenerat Therapies Dresden Dresden Germany

ISBN: (纸本)9781510632639;9781510632646

Identification of different cell types is an indispensable part in biomedical research and clinical application. During the last decades, much attention was put onto molecular characterization and many cell types can now be identified and sorted based on established markers. The required staining process is a lengthy and costly treatment, which can cause alterations of cellular properties, contaminate the sample and therefore limit its subsequent use. A promising alternative to molecular markers is the label-free identification of cells using mechanical or morphological features. We introduce a microfluidic device for active label-free sorting of cells based on their bright field image supported by innovative real-time image processing and deep neural networks (DNNs). A microfluidic chip features a standing surface acoustic wave generator for actively pushing up to 100 cells/sec to a determined outlet for collection. This novel method is successfully applied for enrichment of lymphocytes, granulo-monocytes and red blood cells from human blood. Furthermore, we combined the setup with lasers and a fluorescence detection unit, allowing to assign a fluorescence signal to each captured bright-field image. Leveraging this tool and common molecular staining, we created a labelled dataset containing thousands of images of different blood cells. We used this dataset to train a DNN with optimized latency below 1 ms and used it to sort unstained neutrophils from human blood, resulting in a target concentration of 90%. The innovative approach to use deep learning for image-based sorting opens up a wide field of potential applications, for example label-free enrichment of stem-cells for transplantation.

关键词： Mechanical phenotype label-free cell sorting standing surface acoustic waves (SSAW) artificial intelligence deep neural networks flow cytometry image-based sorting

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