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MRI image denoising for telemedicine

MRI image denoising for telemedicine

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HEALTHCOM 2006: Mobile E-Health for Developing Countries - 2006 8th International conference on e-Health Networking, applications and Services

作者： Parthiban, Latha Subramanian, R. Pondicherry University Department of Computer Science and Engineering Pondicherry University

Removing noise from original image is a challenging problem for researchers. There have been several published algorithms and each approach has its assumptions, advantages and limitations In this paper, we propose a method for noise filtering in MRI images. Due to the signal-dependent mean of the Rician noise that affects MRI images, both wavelet and scaling coefficients of a noisy MRI image are biased estimates of their noise-free counterparts. This problem was overcome by filtering the square of the MRI magnitude image in the wavelet domain. In the squared magnitude image, data are noncentral chi-square distributed, and the wavelet coefficients are no longer biased estimates of their noise-free counterparts. The bias still remains in the scaling coefficients, but is not signal dependent and it can be removed easily;at the resolution scale 2j from each scaling coefficient 2j-1 σc should be subtracted, where σc2is the underlying complex Gaussian noise variance. So, we apply our method to the squared magnitude of the MRI image, subtract the constant bias from the scaling coefficients, and subsequently compute the square root of the denoised squared magnitude image. The application of the proposed method to real noisy MRI images is expected to facilitate further automatic processing, like segmentation and further analysis. MRI image denoising still remains a challenge for researchers, because noise removal introduces artifacts and causes blurring of the images. ©2006 IEEE.

关键词： Telemedicine

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Noise reduction from magnetic resonance images using nonseperable transforms - art. no. 61446F

Noise reduction from magnetic resonance images using nonsepe...

引用

Medical Imaging 2006 conference

作者： Nezhadarya, Ehsan Shamsollahi, Mohammad Bagher Sharif Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)0819464236

Multi-scale transforms have got a lot of applications in image processing, in recent years. wavelet transform is a powerful multiscale transform for denoising noisy signals and images, but the usual two-dimensional separable wavelets are sub-optimal. These separable wavelet transforms can successfully identify zero dimensional singularities in images, but can weakly identify one dimensional singularities such as edges, curves and lines. In this sense, non-separable transforms such as Ridgelet and Curvelet transforms are proposed by Candes and Donoho. The coefficients produced by these non-separable transforms have shown to be sparser than wavelet coefficients. This fact results in better denoising capabilities than wavelet transform. These new non-separable transforms can identify direction in lines and curves, because of special structure of their basis elements. Basically, Magnetic Resonance images are probable to have Rician noise. In some special cases, this kind of noise can be supposed to be white Gaussian noise. In this paper, a new method for denoising MR images is proposed. This method is based on Monoscale Ridgelet transform. It is shown that this transform can successfully denoise MR images embedded in white Gaussian noise. The results are better in comparison with usual wavelet denoising methods, based on both visual perception and signal-to-noise ratio.

关键词： image denoising multiresolution and wavelets magnetic resonance Imaging ridgelet transform

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Pit pattern classification of zoom-endoscopic colon images using wavelet texture features

Pit pattern classification of zoom-endoscopic colon images u...

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IET 3rd International conference MEDSIP 2006: Advances in Medical, signal and Information processing

作者： Hafner, M. Liedlgruber, M. Wrba, F. Gangl, A. Vecsei, A. Uhl, A. Department of Gastroenterology and Hepatology Medical University of Vienna Austria Department of Computer Sciences Salzburg University Austria St. Anna Children's Hospital Vienna Austria Department of Clinical Pathology Medical University of Vienna

ISBN: (纸本)0863416586

wavelet-based techniques for an automated classification of magnifying endoscope images with respect to pit patterns of colon lesions are discussed and compared. Algorithms classically used for wavelet-based classification but developed originally for texture classification applications provide encouraging results but still lack of the accuracy required for sensible clinical usage.

关键词： image analysis

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wavelet transform based coronary blood vessel segmentation using entropy thresholding

Wavelet transform based coronary blood vessel segmentation u...

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IET 3rd International conference MEDSIP 2006: Advances in Medical, signal and Information processing

作者： Sangeetha, M. Nirmala Devi, S. Kumaravel, N. Department of Electronics and Communication Engineering CEG Anna University Chennai 600 025 India

ISBN: (纸本)0863416586

This paper presents a novel method for automatic segmentation of blood vessel in coronary angiographic images. The proposed algorithm composed of two steps, coronary angiogram enhancement process and entropy based thresholding. In the first step, a set of directional basis filters based on dyadic wavelet transform is designed to enhance blood vessels. Gaussian wavelet is used to fix the blood vessel directional information and its associated changes in coronary angiogram and a gradient image is obtained. Secondly the thresholding approach that evaluates 2-D entropy based on gray level gradient co-occurrence matrix is used to segment blood vessel from the background. The result of this method promises the simpleness and flexibility in many image enhancement and segmentation applications.

关键词： Blood vessels

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Dilation matrices for nonseparable bidimensional wavelets

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8th International conference on Advanced Concepts for Intelligent Vision Systems

作者： Ruedin, Ana Univ Buenos Aires Fac Ciencias Exactas & Nat Dept Computac RA-1428 Buenos Aires DF Argentina

ISBN: (纸本)3540446303

For nonseparable bidimensional wavelet transforms, the choice of the dilation matrix is all-important, since it governs the downsampling and upsampling steps, determines the cosets that give the positions of the filters, and defines the elementary set that gives a tesselation of the plane. We introduce nonseparable bidimensional wavelets, and give formulae for the analysis and synthesis of images. We analyze several dilation matrices, and show how the wavelet transform operates visually. We also show some distorsions produced by some of these matrices. We show that the requirement of their eigenvalues being greater than 1 in absolute value is not enough to guarantee their suitability for image processing applications, and discuss other conditions.

关键词： quincunx nonseparable dilation wavelet

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Evolved transforms for signal compression and reconstruction under quantization

Evolved transforms for signal compression and reconstruction...

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conference on Modeling and Simulation for Military applications

作者： Moore, Frank W. Univ Alaska Anchorage Dept Math Sci Anchorage AK 99508 USA

ISBN: (纸本)0819462845

State-of-the-art signal compression and reconstruction techniques utilize wavelets. However, recently published research demonstrated that a genetic algorithm (GA) is capable of evolving non-wavelet inverse transforms that consistently outperform wavelets when used to reconstruct one- and two-dimensional signals under conditions subject to quantization error. This paper summarizes the results of a series of three follow-on experiments. First, a GA is developed to evolve matched forward and inverse transform pairs that simultaneously minimize the compressed file size (FS) and the squared error (SE) in the reconstructed file. Second, this GA is extended to evolve a single set of coefficients that may be used at every level of a multi-resolution analysis (MRA) transform. Third, this GA is expanded to achieve additional SE reduction by evolving a different set of coefficients for each level of an MRA transform. Test results indicate that coefficients evolved against a single representative training image generalize to effectively reduce SE for a broad class of reconstructed images.

关键词： wavelets genetic algorithms quantization multi-resolution analysis image processing

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A geometric mean based adaptive local noise removal algorithm

A geometric mean based adaptive local noise removal algorith...

引用

International Computer conference on wavelet Active Media Technology and Information processing

作者： Sun, Qiao-Ping Zhao, Xiao-Ming Taizhou Univ Dept Comp Taizhou 317000 Zhejiang Peoples R China

ISBN: (纸本)9812700420

Adaptive filters are important application in the signal processing field. This paper discusses the adaptive local noise removal algorithm proposed by Professor Rafael C. Gonzalez and points out its shortcomings. To improve the algorithm, a new method, geometric mean based adaptive local noise removal algorithm, has been proposed. The simulation results indicate that the new algorithm is more satisfactory. The mean square error e(mse) is reduced by 1/4. The signals to noise ratios (i.e., SNR, SNRm, PSRN) are raised by 1/10. This algorithm has been shown promise for applications.

关键词： adaptive filtering geometric mean image de-noising local noise removal

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Telco churn analysis classification using a wavelet and RBF approach

Telco churn analysis classification using a wavelet and RBF ...

引用

7th International conference on Data, Text and Web Mining and their Business applications and Management Information Engineering, DATA MINING 2006, DATA06

作者： Cister, A.M. Ebecken, N.F.F. FACC COPPE/UFRJ Brazil COPPE/UFRJ Federal University of Rio de Janeiro Brazil

ISBN: (纸本)1845641787

The goal of this paper is to present a quantitative study about Telco churn analysis and the inherent problems of its application and computer processing. An independent customer characterizes the approach and the pre-processing step uses the wavelet transform concept;the pattern classification problem is solved by an artificial neural network (RBF- Radial Basis Function). The signal representation considers different levels of resolution (multi-resolution). This idea is frequently used in image/video processing where general image versions with a low resolution are stored in different levels of resolution. The concepts of multi-resolution have been implemented by a digital filter bank, Hi-pass and Low-pass. Each sub-band will give the best resolution, for each spectral region. The idea is to look for a spectral transient. A scalable Daubechies' function db 2 and db 3 were tested for this study and the results were used as input for the RBF neural network. Daubechies' function has shown important properties as for instance, orthogonality and scalability, which are not presented by Morlet's function. Some results of the experiment were presented to point out the performance of the wavelet pre-processing approach.

关键词： Classification (of information)

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Skin surface removal on breast microwave imagery using wavelet multiscale products

Skin surface removal on breast microwave imagery using wavel...

引用

Medical Imaging 2006 conference

作者： Flores-Tapia, Daniel Thomas, Gabriel Pistorius, Stephen Univ Manitoba Dept Elect & Comp Engn Winnipeg MB R3T 2N2 Canada

ISBN: (纸本)0819461865

In many parts of the world, breast cancer is the leading cause mortality among women and it is the major cause of cancer death, next only to lung cancer. In recent years, microwave imaging has shown its potential as an alternative approach for breast cancer detection. Although advances have improved the likelihood of developing an early detection system based on this technology, there are still limitations. One of these limitations is that target responses are often obscured by surface reflections. Contrary to ground penetrating radar applications, a simple reference subtraction cannot be easily applied to alleviate this problem due to differences in the breast skin composition between patients. A novel surface removal technique for the removal of these high intensity reflections is proposed in this paper. This paper presents an algorithm based on the multiplication of adjacent wavelet subbands in order to enhance target echoes while reducing skin reflections. In these multiscale products, target signatures can be effectively distinguished from surface reflections. A simple threshold is applied to the signal in the wavelet domain in order to eliminate the skin responses. This final signal is reconstructed to the spatial domain in order to obtain a focused image. The proposed algorithm yielded promising results when applied to real data obtained from a phantom which mimics the dielectric properties of breast, cancer and skin tissues.

关键词： microwave imagery cancer detection clutter removal biomedical image processing wavelets

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image compression algorithm based on morphological associative memories

引用

11th Iberoamerican conference on Pattern Recognition

作者： Guzman, Enrique Pogrebnyak, Oleksiy Yanez, Cornelio Moreno, Jose A. Univ Tecnol Mixteca Huajuapan de Leon Oaxaca Mexico Inst Politecn Nacl Ctr Invest Comp Mexico City 07738 DF Mexico

ISBN: (纸本)3540465561

A new method for image compression based on Morphological Associative Memories (MAM) is proposed. We used MAM at the transformation stage of image coding, thereby replacing the traditional methods such as Discrete Cosine Transform or wavelet Transform. After applying the MAM, the informative image data are concentrated in a minimum of values. The next stages of image coding can be obtained by taking advantage of this new representation of the image. The main advantage offered by the MAM with respect to the traditional methods is the speed of processing, whereas the compression rate and the obtained signal to noise ratios compete with the traditional methods.

关键词： image compression morphological associative memories morphological hetroassociative memories min.

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