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Titlebook: Despeckle Filtering Algorithms and Software for Ultrasound Imaging; Christos P. Loizou,Constantinos S. Pattichis Book 2008 Springer Nature

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發(fā)表于 2025-3-21 17:33:39 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Despeckle Filtering Algorithms and Software for Ultrasound Imaging
編輯Christos P. Loizou,Constantinos S. Pattichis
視頻videohttp://file.papertrans.cn/270/269142/269142.mp4
叢書名稱Synthesis Lectures on Algorithms and Software in Engineering
圖書封面Titlebook: Despeckle Filtering Algorithms and Software for Ultrasound Imaging;  Christos P. Loizou,Constantinos S. Pattichis Book 2008 Springer Nature
描述It is well-known that speckle is a multiplicative noise that degrades image quality and the visual evaluation in ultrasound imaging. This necessitates the need for robust despeckling techniques for both routine clinical practice and teleconsultation. The goal for this book is to introduce the theoretical background (equations), the algorithmic steps, and the MATLAB? code for the following group of despeckle filters: linear filtering, nonlinear filtering, anisotropic diffusion filtering and wavelet filtering. The book proposes a comparative evaluation framework of these despeckle filters based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts, in the assessment of cardiovascular ultrasound images recorded from the carotid artery. The results of our work presented in this book, suggest that the linear local statistics filter DsFlsmv, gave the best performance, followed by the nonlinear geometric filter DsFgf4d, and the linear homogeneous maskarea filter DsFlsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes (of ultrasound images recorded from the carotid artery for the assessment of st
出版日期Book 2008
版次1
doihttps://doi.org/10.1007/978-3-031-01510-6
isbn_ebook978-3-031-01510-6Series ISSN 1938-1727 Series E-ISSN 1938-1735
issn_series 1938-1727
copyrightSpringer Nature Switzerland AG 2008
The information of publication is updating

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Despeckle Filtering Algorithms and Software for Ultrasound Imaging
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Book 2008est performance, followed by the nonlinear geometric filter DsFgf4d, and the linear homogeneous maskarea filter DsFlsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes (of ultrasound images recorded from the carotid artery for the assessment of st
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Gadamer, Beauty, and Musical Improvisatione measures, the univariate statistical analysis, and the .-nearest-neighbor (kNN) classifier, which are used to evaluate despeckle filtering. Also, a number of image quality metrics are presented for evaluating the quality between the original and despeckled images. Finally, the procedure of visual
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https://doi.org/10.1007/978-3-031-41570-8 in the case of image quality evaluation and visual evaluation by the experts. In this study, a total of 10 despeckle filters were comparatively evaluated on 440 ultrasound images of the carotid artery bifurcation, and the validation results are summarized in Table 5.1.
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