- Email: [email protected]
Introduction to the 6th special section on Advances in Biomedical Signal and Image Processing, and Biometrics
Computers and Electrical Engineering 53 (2016) 122–124
Contents lists available at ScienceDirect
Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng
Editorial
Introduction to the 6th special section on Advances in Biomedical Signal and Image Processing, and Biometrics
In parallel with the advancement in technology and evolution of computerized systems biomedical engineering has reached its golden age. This has particularly improved acquisition and processing of signals from single sensor to a network of body sensors and image processing modalities ranging from digital x-rays to the amazing domain of molecular imaging. There are a wide range of techniques that can be used for measurement and monitoring of the state of the patients during the treatment. The new medical technologies demand for development of more complex signal and image processing approaches to meet such sophisticated developments. The new medical data acquisition systems provide anatomical representation of body organs and their functional activities with higher resolution in compared to the past. On the other hand, molecular imaging has its own demand for advances in technology and data processing. This means that the size of the data to be evaluated for the diagnosis has become large as well (for example functional magnetic resonance imaging (fMRI) data provides a 3-dimensional image of more than 10 0,0 0 0 voxels per each time scan, a whole brain electroencephalography (EEG) provides a multichannel signal of high dimension, etc.). This in turn makes the problem of reading, investigating and interpreting the data for a physician much more difficult and the demand for processing of such big data over multimode clusters becomes inevitable. Therefore, fast and more accurate clinical diagnosis requires automatic engineering techniques for the analysis, processing, feature extraction and classification of medical signal and images. This is the sixth special section on this topic published in this Journal thus far. The guest editors of the current special section have already published two previous special sections [1, 2] covering some of the advancements in the field of biomedical signal and image processing and biometrics. Due to the limited space we could not accommodate all the topics rising in this field, but we have selected the best submitted manuscripts covering some of the developments in the domain. A short description of each paper in this special section is given here. The first 4 papers in this special issue deal with processing of EEG signals. EEG system provides a cheap, portable and non-invasive way for recording of brain electrical activities and can be used for many biomedical applications including the medical diagnosis. An essential step toward the analysis of EEG signals artifact removal and noise suppression. A novel artifact removal and noise suppression method has been proposed by Upadhyay et al. They use Independent Component Analysis (ICA) and Discrete Orthonormal S-Transform for their denoising approach. The effectiveness of the method has been shown for simulated and real EEG data. The focus of the other 3 papers is to use EEG signals for seizure detection and prediction. Zainuddin et al. suggests an enhanced harmony search based method for feature selection in EEG signals for epileptic seizure detection and prediction. They have used wavelet neural networks for their classification part. In the other paper, Upadhyay, Padhy and Kumar used a proper feature ranking approach to obtain optimal wavelet function and wavelet based features for epilepsy detection in EEG signals. They evaluated different statistical, fractal and entropy based features in wavelet coefficients. Least Square-Support Vector Machine (LS-SVM) was used for the classification section. Their results showed that feature ranking using Fisher score outperformed the other approaches for the application of epileptic seizure detection. A new hybrid automated epileptic seizure detection method is introduced by Tawfik et al. They consider the combination of Weighted Permutation Entropy and an SVM classifier for seizure detection. Their method shows high robustness against noise in addition to obtaining a high performance of the algorithm. The next 3 papers consider the analysis of Electrocardiogram (ECG) signals. An ECG is a rich non-invasive source for evaluating different cardiac diseases. Monitoring of patients using the ECG systems is a very common step in many hospitals and health care institutions. Hanlin Chen proposes a real-time, remote ECG system with high reliability and good swiftness. A combination of FPGA A2F500 and GSM/GPRS engine SIM508 has been used for this monitoring system. Parallel PCAICA Algorithm has been applied for preprocessing and feature extraction phase in this system which it can be used for http://dx.doi.org/10.1016/j.compeleceng.2016.09.020 0045-7906/© 2015 Published by Elsevier B.V.
Editorial / Computers and Electrical Engineering 53 (2016) 122–124
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fast diagnoses. An important issue in wireless tele-monitoring applications is the energy-efficiency. This issue has been considered by Singh and Dandapat. They have used multi-channel compressed sensing method to address the computational complexity of tele-monitoring in multi-channel ECG signals. Their proposed algorithm is to recover the ECG signals using weighted mixed norm minimization in the wavelet domain for extracting the joint sparsity in multichannels. Their results indicate that the proposed approach provides a good reconstruction quality in multi-channel ECG signals. In the last paper of this group, Mateo et al. presented a method for ECG beat classification. The method is based on canceling out ectopics beats which are common in atrial fibrillation. They used Radial Basis Function Neural Network for classification of normal and abnormal beats and improved the accuracy of beat classification. In a relevant study to the previous ones (heart system), Guan-Chun Chen designed a virtual reality simulator for interventional cardiac catheterization in his proposed mechanical training system. The system can be used for training surgeons in medical education. There are seven papers related to biomedical image analysis from different modalities including computed tomography, angiograms, MRI, microCT, echocardiography and x-ray images in this issue. The first two papers in this part consider the important issue of medical image segmentation. Segmentation of cancerous regions in liver has been studied by Sethi et al. They proposed edge-based and phase congruent region enhancement for segmentation of cancerous regions. The cancerous region is first described using the phase map and it is then thresholded to create an edge map and after edge enhancement at the boundaries is converted into feature map. The final image is generated by combination of feature map and the original image. Distance regularised level set evolution with a new stopping function is used for the final segmentation. Cruz-Aceves et al. suggested a method for segmentation of coronary angiograms. The method is automatic and uses a Gaussian matched filter based on entropy minimization. Hasan and Meziane used grey level statistics for automatic brain MR image scanning. They proposed a method based on prior knowledge of bilateral symmetry in healthy brain for classification of brain slices into normal and abnormal cases. For the measure of symmetry between the two hemispheres, they used a modified version of grey level co-occurrence matrix. They were able to detect the abnormalities with high accuracy with their approach. In another study related to biomedical images, Rapesta et al. introduced a compression strategy for microCTs. In this approach each volumes of interest was coded independently and then they were grouped in a DICOM-compliant file. The method was then evaluated on bone morphometry assessment in microCT images but it can be extended to other applications and image modalities. Gifani et al. in another paper in the group of image analysis, proposed an approach for noise reduction in echocardiography images. Their approach uses a filtering method based on temporal information and sparse representation. The results of using the approach indicate that it can reduce the speckle noise while preserving the edge information. Xu et al. in another study proposed a lossless coding for X-ray angiography images. This method is based on based on automatic segmentation of the focal area using ray-casting and α -shapes. The region of interest is selected using symmetrical features and the background is suppressed. The obtained image is then encoded using JPEG-LS, JPEG20 0 0, H.264 and HEVC methods. In the last paper in biomedical image analysis part a watermarking method for medical images is introduced by Chinnababu and Natarajan which uses difference expansion. The proposed method is a reversible watermarking approach. The last two papers are related to biometrics. In the first one by Abo-Zahhad et al. proposed a method of biometrics from heart sound. The proposed method is based on wavelet packet cepstral features. An automatic de-noising approach has been used in the first step using wavelet transform. The energy features are then extracted using wavelet packet decomposition. The features are then classified using linear discriminant analysis. The focus of the last paper is Retina-based human recognition. Waheed et al. proposed a person identification scheme using two approaches including vascular and non-vascular Retinal features. The first one uses a vessel segmentation method and applies vessel properties of retinal images for identification while non-vascular method considers non-vessel properties of retinal images. The proposed methods have been evaluated both using local and public databases. We hope that the manuscripts published in this special issue provide a seed for thought and useful information for the researchers in the field of biomedical signal and image processing and biometrics. Guest Editors
Mohammad Reza Daliri Biomedical Engineering Department, Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), and the School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran Saeid Sanei Faculty of Engineering and Physical Sciences, University of Surrey, UK E-mail addresses: [email protected] (M.R. Daliri), [email protected] (S. Sanei)
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Editorial / Computers and Electrical Engineering 53 (2016) 122–124
References [1] Daliri MR, Sanei S. Editorial: Introduction to the special issue on Advances in Biomedical Signal and Image Processing, and Biometrics. Computers and Electrical Engineering 2014;40:1714–16. [2] Daliri MR, Jin Z. Editorial: Introduction to the special issue on Advances in Biomedical Signal and Image Processing, and Biometrics. Computers and Electrical Engineering 2015;45:208–10. Mohammad Reza Daliri received his PhD from the International School for Advanced Studies (ISAS/SISSA), Trieste, Italy, in 2007. After his PhD, he received a funding from International Center for Theoretical Physics (ICTP), Trieste, Italy as an opportunity to undertake training and research in an Italian laboratory (TRIL programme). He then moved to Cognitive Neuroscience Laboratory, German Primate Center (DPZ), Goettingen, Germany as a postdoctoral researcher. Since then he has been a member of academic staff in Iran. Currently he is an Associate Professor in the Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. He has served as a member of editorial boards for several international journals and a member of technical program committees of different international conferences. His main research interests include brain signal processing, computational and cognitive neuroscience, pattern recognition, and computer vision (mainly for biomedical applications).
Saeid Sanei received his PhD from Imperial College London in 1991. Since then he has been a member of academic staff in Iran, Singapore, and the United Kingdom. Recently, he has been the Deputy Head of Computing Department, Faculty of Engineering and Physical Sciences, University of Surrey, UK. He is the author of three monograms in biomedical signal processing and over 320 peer reviewed papers. His research interests include adaptive and nonlinear signal processing and machine learning applied mainly to biomedical signals and images.
Contents lists available at ScienceDirect
Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng
Editorial
Introduction to the 6th special section on Advances in Biomedical Signal and Image Processing, and Biometrics
In parallel with the advancement in technology and evolution of computerized systems biomedical engineering has reached its golden age. This has particularly improved acquisition and processing of signals from single sensor to a network of body sensors and image processing modalities ranging from digital x-rays to the amazing domain of molecular imaging. There are a wide range of techniques that can be used for measurement and monitoring of the state of the patients during the treatment. The new medical technologies demand for development of more complex signal and image processing approaches to meet such sophisticated developments. The new medical data acquisition systems provide anatomical representation of body organs and their functional activities with higher resolution in compared to the past. On the other hand, molecular imaging has its own demand for advances in technology and data processing. This means that the size of the data to be evaluated for the diagnosis has become large as well (for example functional magnetic resonance imaging (fMRI) data provides a 3-dimensional image of more than 10 0,0 0 0 voxels per each time scan, a whole brain electroencephalography (EEG) provides a multichannel signal of high dimension, etc.). This in turn makes the problem of reading, investigating and interpreting the data for a physician much more difficult and the demand for processing of such big data over multimode clusters becomes inevitable. Therefore, fast and more accurate clinical diagnosis requires automatic engineering techniques for the analysis, processing, feature extraction and classification of medical signal and images. This is the sixth special section on this topic published in this Journal thus far. The guest editors of the current special section have already published two previous special sections [1, 2] covering some of the advancements in the field of biomedical signal and image processing and biometrics. Due to the limited space we could not accommodate all the topics rising in this field, but we have selected the best submitted manuscripts covering some of the developments in the domain. A short description of each paper in this special section is given here. The first 4 papers in this special issue deal with processing of EEG signals. EEG system provides a cheap, portable and non-invasive way for recording of brain electrical activities and can be used for many biomedical applications including the medical diagnosis. An essential step toward the analysis of EEG signals artifact removal and noise suppression. A novel artifact removal and noise suppression method has been proposed by Upadhyay et al. They use Independent Component Analysis (ICA) and Discrete Orthonormal S-Transform for their denoising approach. The effectiveness of the method has been shown for simulated and real EEG data. The focus of the other 3 papers is to use EEG signals for seizure detection and prediction. Zainuddin et al. suggests an enhanced harmony search based method for feature selection in EEG signals for epileptic seizure detection and prediction. They have used wavelet neural networks for their classification part. In the other paper, Upadhyay, Padhy and Kumar used a proper feature ranking approach to obtain optimal wavelet function and wavelet based features for epilepsy detection in EEG signals. They evaluated different statistical, fractal and entropy based features in wavelet coefficients. Least Square-Support Vector Machine (LS-SVM) was used for the classification section. Their results showed that feature ranking using Fisher score outperformed the other approaches for the application of epileptic seizure detection. A new hybrid automated epileptic seizure detection method is introduced by Tawfik et al. They consider the combination of Weighted Permutation Entropy and an SVM classifier for seizure detection. Their method shows high robustness against noise in addition to obtaining a high performance of the algorithm. The next 3 papers consider the analysis of Electrocardiogram (ECG) signals. An ECG is a rich non-invasive source for evaluating different cardiac diseases. Monitoring of patients using the ECG systems is a very common step in many hospitals and health care institutions. Hanlin Chen proposes a real-time, remote ECG system with high reliability and good swiftness. A combination of FPGA A2F500 and GSM/GPRS engine SIM508 has been used for this monitoring system. Parallel PCAICA Algorithm has been applied for preprocessing and feature extraction phase in this system which it can be used for http://dx.doi.org/10.1016/j.compeleceng.2016.09.020 0045-7906/© 2015 Published by Elsevier B.V.
Editorial / Computers and Electrical Engineering 53 (2016) 122–124
123
fast diagnoses. An important issue in wireless tele-monitoring applications is the energy-efficiency. This issue has been considered by Singh and Dandapat. They have used multi-channel compressed sensing method to address the computational complexity of tele-monitoring in multi-channel ECG signals. Their proposed algorithm is to recover the ECG signals using weighted mixed norm minimization in the wavelet domain for extracting the joint sparsity in multichannels. Their results indicate that the proposed approach provides a good reconstruction quality in multi-channel ECG signals. In the last paper of this group, Mateo et al. presented a method for ECG beat classification. The method is based on canceling out ectopics beats which are common in atrial fibrillation. They used Radial Basis Function Neural Network for classification of normal and abnormal beats and improved the accuracy of beat classification. In a relevant study to the previous ones (heart system), Guan-Chun Chen designed a virtual reality simulator for interventional cardiac catheterization in his proposed mechanical training system. The system can be used for training surgeons in medical education. There are seven papers related to biomedical image analysis from different modalities including computed tomography, angiograms, MRI, microCT, echocardiography and x-ray images in this issue. The first two papers in this part consider the important issue of medical image segmentation. Segmentation of cancerous regions in liver has been studied by Sethi et al. They proposed edge-based and phase congruent region enhancement for segmentation of cancerous regions. The cancerous region is first described using the phase map and it is then thresholded to create an edge map and after edge enhancement at the boundaries is converted into feature map. The final image is generated by combination of feature map and the original image. Distance regularised level set evolution with a new stopping function is used for the final segmentation. Cruz-Aceves et al. suggested a method for segmentation of coronary angiograms. The method is automatic and uses a Gaussian matched filter based on entropy minimization. Hasan and Meziane used grey level statistics for automatic brain MR image scanning. They proposed a method based on prior knowledge of bilateral symmetry in healthy brain for classification of brain slices into normal and abnormal cases. For the measure of symmetry between the two hemispheres, they used a modified version of grey level co-occurrence matrix. They were able to detect the abnormalities with high accuracy with their approach. In another study related to biomedical images, Rapesta et al. introduced a compression strategy for microCTs. In this approach each volumes of interest was coded independently and then they were grouped in a DICOM-compliant file. The method was then evaluated on bone morphometry assessment in microCT images but it can be extended to other applications and image modalities. Gifani et al. in another paper in the group of image analysis, proposed an approach for noise reduction in echocardiography images. Their approach uses a filtering method based on temporal information and sparse representation. The results of using the approach indicate that it can reduce the speckle noise while preserving the edge information. Xu et al. in another study proposed a lossless coding for X-ray angiography images. This method is based on based on automatic segmentation of the focal area using ray-casting and α -shapes. The region of interest is selected using symmetrical features and the background is suppressed. The obtained image is then encoded using JPEG-LS, JPEG20 0 0, H.264 and HEVC methods. In the last paper in biomedical image analysis part a watermarking method for medical images is introduced by Chinnababu and Natarajan which uses difference expansion. The proposed method is a reversible watermarking approach. The last two papers are related to biometrics. In the first one by Abo-Zahhad et al. proposed a method of biometrics from heart sound. The proposed method is based on wavelet packet cepstral features. An automatic de-noising approach has been used in the first step using wavelet transform. The energy features are then extracted using wavelet packet decomposition. The features are then classified using linear discriminant analysis. The focus of the last paper is Retina-based human recognition. Waheed et al. proposed a person identification scheme using two approaches including vascular and non-vascular Retinal features. The first one uses a vessel segmentation method and applies vessel properties of retinal images for identification while non-vascular method considers non-vessel properties of retinal images. The proposed methods have been evaluated both using local and public databases. We hope that the manuscripts published in this special issue provide a seed for thought and useful information for the researchers in the field of biomedical signal and image processing and biometrics. Guest Editors
Mohammad Reza Daliri Biomedical Engineering Department, Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), and the School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran Saeid Sanei Faculty of Engineering and Physical Sciences, University of Surrey, UK E-mail addresses: [email protected] (M.R. Daliri), [email protected] (S. Sanei)
124
Editorial / Computers and Electrical Engineering 53 (2016) 122–124
References [1] Daliri MR, Sanei S. Editorial: Introduction to the special issue on Advances in Biomedical Signal and Image Processing, and Biometrics. Computers and Electrical Engineering 2014;40:1714–16. [2] Daliri MR, Jin Z. Editorial: Introduction to the special issue on Advances in Biomedical Signal and Image Processing, and Biometrics. Computers and Electrical Engineering 2015;45:208–10. Mohammad Reza Daliri received his PhD from the International School for Advanced Studies (ISAS/SISSA), Trieste, Italy, in 2007. After his PhD, he received a funding from International Center for Theoretical Physics (ICTP), Trieste, Italy as an opportunity to undertake training and research in an Italian laboratory (TRIL programme). He then moved to Cognitive Neuroscience Laboratory, German Primate Center (DPZ), Goettingen, Germany as a postdoctoral researcher. Since then he has been a member of academic staff in Iran. Currently he is an Associate Professor in the Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. He has served as a member of editorial boards for several international journals and a member of technical program committees of different international conferences. His main research interests include brain signal processing, computational and cognitive neuroscience, pattern recognition, and computer vision (mainly for biomedical applications).
Saeid Sanei received his PhD from Imperial College London in 1991. Since then he has been a member of academic staff in Iran, Singapore, and the United Kingdom. Recently, he has been the Deputy Head of Computing Department, Faculty of Engineering and Physical Sciences, University of Surrey, UK. He is the author of three monograms in biomedical signal processing and over 320 peer reviewed papers. His research interests include adaptive and nonlinear signal processing and machine learning applied mainly to biomedical signals and images.