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An emboli detection system based on dual tree complex wavelet transform and ensemble learning

APPLIED SOFT COMPUTING 2015年 37卷 87-94页

作者： Serbes, Gorkem Sakar, Betul Erdogdu Gulcur, Halil Ozcan Aydin, Nizamettin Yildiz Tech Univ Dept Biomed Engn TR-34220 Istanbul Turkey Bahcesehir Univ Software Engn Dept Istanbul Turkey Bogazici Univ Inst Biomed Engn Istanbul Turkey Yildiz Tech Univ Dept Comp Engn TR-34220 Istanbul Turkey

The traditional visual and acoustic embolic signal detection methods based on the expert analysis of individual spectral recordings and Doppler shift sounds are the gold standards. However, these types of detection methods are high-cost, subjective, and can only be applied by experts. In order to overcome these drawbacks, computer based automated embolic detection systems which employ spectral properties of emboli, speckle, and artifact using Fourier and wavelet transforms have been proposed. In this study, we propose a fast, accurate, and robust automated emboli detection system based on the dual tree complex wavelet transform (DTCWT). Employing the DTCWT, which does not suffer from the lack of shift invariance property of ordinary Discrete wavelet transform (DWT), increases the robustness of the coefficients extracted from the Doppler ultrasound signals. In this study, a Doppler ultrasound dataset including 100 samples from each embolic, Doppler speckle, and artifact signal is used. Each sample obtained from forward and reverse blood flow directions is represented by 1024 points. In our method, we first extract the forward and reverse blood flow coefficients separately using DTCWT from the samples. Then dimensionality reduction is applied to each set of coefficients and both of the reduced set of coefficients are fed to classifiers individually. Subsequently, in the view that the forward and reverse blood flow coefficients carry different characteristics, the individual predictors of these classifiers are combined using ensemble stacking method. We compare the obtained results with Fast Fourier transform and DWT based emboli detection systems, and show that the features extracted using DTCWT give the highest accuracy and emboli detection rate. It is also observed that combining forward and reverse coefficients using stacking ensemble method improves the emboli and artifact detection rates, and overall accuracy. (C) 2015 Elsevier B.V. All rights reserved.

关键词： dual tree complex wavelet transform Embolic signals Dimensionality reduction Support vector machines Stacked generalization Ensemble learning

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A palmprint based biometric authentication system using dual tree complex wavelet transform

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MEASUREMENT 2013年第10期46卷 4179-4188页

作者： Chakraborty, Soumyasree Bhattacharya, Indrani Chatterjee, Amitava Jadavpur Univ Dept Elect Engn Kolkata 700032 W Bengal India

Palmprint identification has emerged as one of the most popular and promising biometric modalities for personal identity verification due to its ease of acquisition, non-invasive procedure, high user acceptance and reliability. This paper proposes the development of a new method for palmprint based biometric authentication which utilizes the textural information available on the palmprint by employing the dual tree complex wavelet transform (DTCWT). The method proposes to construct a region of interest (ROI) for the scanned color images of the palm, and then determine a histogram of the two dimensional image. This enables to utilize a feature extraction module, implemented using the one-dimensional (1D) dual tree complex wavelet transform (DTCWT) on the histogram signal. The DTCWT is an improvement over the discrete wavelet transform (DWT) as it provides nearly shift invariant performance, reduced aliasing and directional wavelets in higher dimensions. Backpropagation neural-network (BPNN) based binary classifiers are developed for authentication utilizing the features extracted. The system is developed on the basis of several scanned color images of palms of individuals in real-life, in our laboratory. The experimental results obtained from the data have demonstrated the utility of the proposed system, by exhibiting an overall mean accuracy as high as 98.35%. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Biometric verification Palmprint dual tree complex wavelet transform Histogram Backpropagation neural networks (BPNNs)

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Application of dual tree complex wavelet transform in tandem mass spectrometry

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COMPUTERS IN BIOLOGY AND MEDICINE 2015年 63卷 36-41页

作者： Murugesan, Selvaraaju Tay, David B. H. Cooke, Ira Faou, Pierre La Trobe Univ Dept Elect Engn Bundoora Vic 3086 Australia La Trobe Univ Dept Biochem Bundoora Vic 3086 Australia

Mass Spectrometry (MS) is a widely used technique in molecular biology for high throughput identification and sequencing of peptides (and proteins). Tandem mass spectrometry (MS/MS) is a specialised mass spectrometry technique whereby the sequence of peptides can be determined. Preprocessing of the MS/MS data is indispensable before performing any statistical analysis on the data. In this work, preprocessing of MS/MS data is proposed based on the dual tree complex wavelet transform (DTCWT) using almost symmetric Hilbert pair of wavelets. After the preprocessing step, the identification of peptides is done using the database search approach. The performance of the proposed preprocessing technique is evaluated by comparing its performance against Discrete wavelet transform (DWT) and Stationary wavelet transform (SWT). The preprocessing performed using DTCWT identified more peptides compared to DWT and SWT. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Tandem mass spectrometry Proteomic data processing dual tree complex wavelet transform Signal denoising Peptides detection

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Image retrieval based on non-uniform bins of color histogram and dual tree complex wavelet transform

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第14期76卷 15885-15921页

作者： Varish, Naushad Pradhan, Jitesh Pal, Arup Kumar Indian Sch Mines Dhanbad Dept Comp Sci & Engn Dhanbad 826004 Jharkand India

Traditional Content-Based Image Retrieval (CBIR) systems were developed for retrieving similar kinds of images from a whole image database based on the given query image. In this paper, the authors have proposed a hierarchical approach for designing a CBIR scheme based on the color and texture features of an image. Initially, a color based approach is adopted and the intermediate results produced by using these color features is appropriate to discard a significant number of non-relevant images from the database. The intermediate database will be the input for the second stage. At this stage, a texture based approach is adopted for retrieving images from the intermediate database. The color features are extracted by computing the statistical parameters of non-uniform quantized histograms of HSV color space while a rotation invariant multi-resolution texture based approach is accomplished on value(V) component of HSV color space for extracting texture features. These texture features are extracted based on the principal texture direction and by taking the energies from various sub-bands of a dual tree complex wavelet transform (DT-CWT). Furthermore, the proposed scheme is suitable to handle mirror images during the retrieval process. The presented scheme has reduced the processing cost due to the consideration of a hierarchical approach. The proposed scheme is tested on the two well-known Corel-1K and GHIM-10K image databases respectively and satisfactory results were achieved in terms of precision, recall and F-score. The proposed scheme is compared with some other existing state of art CBIR schemes and the experimental results validate the improvement over other schemes in most of the instances.

关键词： Content-based image retrieval Color histogram dual tree complex wavelet transform Principal texture direction Statistical parameters

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Hallucinating faces based on adaptive neighborhood selection and dual tree complex wavelet transform

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OPTIK 2016年第2期127卷 525-534页

作者： Deng, Yu Liu, Fang Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Peoples R China

In this paper, a novel face hallucination method is proposed. Instead of utilizing the gray values of pixels directly, the feature vector extracted using dual tree complex wavelet transform (DTCWT) is exploited to describe the directional detailed information of facial image. Then, the adaptive neighborhood selection (ANS) algorithm is used to preserve neighborhood relationship between the low-resolution and high resolution face by selecting the optimal neighbors of each facial patch from the feature space. The selected neighborhood can well reflect the local geometrical structure of face manifold, so that the linear subspace determined by the optimal linear fitting can approximate the local facial geometry with a higher accuracy. In order to reduce the effect of misalignment, a face alignment method based on three facial keypoints is also used. Experimental results show that the proposed face hallucination method outperforms other state-of-the-art methods in terms of visual inspection and objective evaluations, especially when the facial images are compressed and have low-resolution. (C) 2015 Published by Elsevier GmbH.

关键词： Face hallucination Super-resolution Face alignment dual tree complex wavelet transform Adaptive neighborhood selection

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Fractional dual tree complex wavelet transform and its application to biometric security during communication and transmission

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2012年第1期28卷 254-267页

作者： Bhatnagar, Gaurav Wu, Jonathan Raman, Balasubramanian Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Indian Inst Technol Dept Math Roorkee 247667 Uttar Pradesh India

In this paper, the dual tree complex wavelet transform, which is an important tool and recent advancement in signal and image processing, has been generalized by coalescing dual tree complex wavelet transform and fractional Fourier transform. The new transform, i.e. the fractional dual tree complex wavelet transform (FrDT-CWT) inherits the excellent mathematical properties of dual tree complex wavelet transform and fractional Fourier transform. Possible applications of the proposed transform are in biometrics, image compression, image transmission, transient signal processing etc. In this paper, biometric is chosen as the primary application and hence a new technique is proposed for securing biometrics during communication and transmission over insecure channel. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Fourier transform wavelet transform dual tree complex wavelet transform Biometrics Security Encryption

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Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals

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COMPUTERS IN BIOLOGY AND MEDICINE 2017年 83卷 48-58页

作者： Sudarshan, Vidya K. Acharya, U. Rajendra Oh, Shu Lih Adam, Muhammad Tan, Jen Hong Chua, Chua Kuang Chua, Kok Poo Tan, Ru San Ngee Ann Polytech Dept Elect & Comp Engn Singapore Singapore SIM Univ Dept Biomed Engn Sch Sci & Technol Singapore Singapore Univ Malaya Dept Biomed Engn Fac Engn Kuala Lumpur Malaysia Natl Heart Ctr Dept Cardiol Singapore Singapore

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2 s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (ICNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2 s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.

关键词： Congestive heart failure Electrocardiogram dual tree complex wavelet transform Statistical features K-nearest neighbor Decision tree

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Code generator for implementing dual tree complex wavelet transform on reconfigurable architectures for mobile applications

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HEALTHCARE TECHNOLOGY LETTERS 2016年第3期3卷 184-188页

作者： Canbay, Ferhat Levent, Vecdi Emre Serbes, Gorkem Ugurdag, H. Fatih Goren, Sezer Aydin, Nizamettin Yildiz Tech Univ Comp Engn TR-34220 Istanbul Turkey Ozyegin Univ Comp Engn TR-34794 Istanbul Turkey Yildiz Tech Univ Biomed Engn TR-34220 Istanbul Turkey Ozyegin Univ Elect & Elect Engn TR-34794 Istanbul Turkey Yeditepe Univ Comp Engn TR-34755 Istanbul Turkey

The authors aimed to develop an application for producing different architectures to implement dual tree complex wavelet transform (DTCWT) having near shift-invariance property. To obtain a low-cost and portable solution for implementing the DTCWT in multi-channel real-time applications, various embedded-system approaches are realised. For comparison, the DTCWT was implemented in C language on a personal computer and on a PIC microcontroller. However, in the former approach portability and in the latter desired speed performance properties cannot be achieved. Hence, implementation of the DTCWT on a reconfigurable platform such as field programmable gate array, which provides portable, low-cost, low-power, and high-performance computing, is considered as the most feasible solution. At first, they used the system generator DSP design tool of Xilinx for algorithm design. However, the design implemented by using such tools is not optimised in terms of area and power. To overcome all these drawbacks mentioned above, they implemented the DTCWT algorithm by using Verilog Hardware Description Language, which has its own difficulties. To overcome these difficulties, simplify the usage of proposed algorithms and the adaptation procedures, a code generator program that can produce different architectures is proposed.

关键词： C language DTCWT algorithm PIC microcontroller Verilog Xilinx code generator program dual tree complex wavelet transform embedded-system approach field programmable gate array field programmable gate arrays generator DSP design tool hardware description language hardware description languages high-performance computing medical signal processing mobile applications mobile computing multichannel real-time application personal computer wavelet transforms

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Comparison of Effectiveness of dual tree complex wavelet transform and Anisotropic Diffusion in MCA for Concrete Crack Detection

Comparison of Effectiveness of Dual Tree Complex Wavelet Tra...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Dixit, Ankur Wagatsuma, Hiroaki Kyushu Inst Technol Grad Sch Life Sci & Syst Engn Wakamatsu Ku Kitakyushu Fukuoka Japan Kyushu Inst Technol Grad Sch Life Sci & Syst Engn Kitakyushu Fukuoka Japan RIKEN Brain Sci Inst Saitama Japan AIST Artificial Intelligence Res Ctr Tokyo Japan

ISBN: (纸本)9781538666500

Detection of micro cracks present on the concrete surface of a bridge deck is an important task to maintain the quality, reliability and the health of the bridge. In manual inspection humans inspect the sites, prepare the sketches of cracks manually and note the conditions of irregularities. But this process is very long and does not give the guarantee for accuracy. To overcome the demerits of the manual inspection, in our work micro crack detection in parts of concrete bridge is done by using a drone with the high-resolution proximity camera. However, due to micro vibrations present on the bridge it is very difficult to take the fine pictures of some parts of the bridge and due to intensity and contrast instability, the mm width measurement of concrete cracks is also very difficult. It requires the study of texture features of the images such as variance of gray scale distribution, anisotropic structure, directionality and orientations of local textures etc. To study the texture features of the images of bridge deck we have used Morphological Component Analysis (MCA) based on sparse coding. Every image is decomposed into its coarse and fine components. dual tree complex wavelet transform (DTCWT) and Anisotropic Diffusion (AD) have been used in MCA to find the coarse components of the images acquired by camera. The coarse component is subtracted from the original image and the subtracted component is treated by the Sobel edge detector to demonstrate the cracks in a fine way. Results are compared to determine the best dictionary to find coarse component and the cracks present in the image. The compared results illustrate the quality of selection of dictionary to detect the cracks.

关键词： Anisotropic Diffusion coarse component dual tree complex wavelet transform Discrete wavelet transform Morphological Component Analysis Sobel edge detector

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Object recognition based on dual tree complex wavelet transform

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Research Journal of Applied Sciences, Engineering and Technology 2014年第21期7卷 4621-4626页

作者： Elakkiya, S. Audithan, S. PRIST University Tanjore Tamilnadu India

Automated recognition of objects from images plays an important role in many computer vision systems such as robot navigation, object manipulation and content based image retrieval. In this study, an approach for object recognition based on dual tree complex wavelet transform (DTCWT) is proposed. The proposed approach attempts to extract the detailed information of objects from the multi scale representation by DTCWT. The proposed system is tested on Columbia Object Image Library (COIL-100). All the objects are considered for the classification based on nearest neighbor classifier. The results show that the maximum recognition accuracy achieved by the proposed approach is 97.03%. © Maxwell Scientific Organization, 2014.

关键词： dual tree complex wavelet transform Nearest neighbor classifier Object recognition wavelet transform

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