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001 978-3-031-01510-6
003 DE-He213
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008 221116s2008 sz | s |||| 0|eng d
020 _a9783031015106
_9978-3-031-01510-6
024 7 _a10.1007/978-3-031-01510-6
_2doi
050 4 _aTK5102.9
072 7 _aTJF
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC067000
_2bisacsh
072 7 _aTJF
_2thema
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082 0 4 _a621.382
_223
100 1 _aLoizou, Christos.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987403
245 1 0 _aDespeckle Filtering Algorithms and Software for Ultrasound Imaging
_h[electronic resource] /
_cby Christos Loizou, Constantinos Pattichis.
250 _a1st ed. 2008.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2008.
300 _aIV, 166 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Algorithms and Software in Engineering,
_x1938-1735
505 0 _aIntroduction to Ultrasound Imaging -- Despeckle Filtering Algorithms -- Evaluation Methodology -- Applications of Despeckle Filtering in Ultrasound Imaging -- Comparison and Discussion of Despeckle Filtering Algorithms -- Summary and Future Directions.
520 _aIt 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 stroke) based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by two medical experts. A despeckle filtering analysis and evaluation framework is proposed for selecting the most appropriate filter or filters for the images under investigation. These filters can be further developed and evaluated at a larger scale and in clinical practice in the automated image and video segmentation, texture analysis, and classification not only for medical ultrasound but for other modalities as well, such as synthetic aperture radar (SAR) images. Table of Contents: Introduction to Ultrasound Imaging / Despeckle Filtering Algorithms / Evaluation Methodology / Applications of Despeckle Filtering in Ultrasound Imaging / Comparison and Discussion of Despeckle Filtering Algorithms / Summary and Future Directions.
650 0 _aSignal processing.
_94052
650 1 4 _aSignal, Speech and Image Processing.
_931566
700 1 _aPattichis, Constantinos.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987405
710 2 _aSpringerLink (Online service)
_987407
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031003820
776 0 8 _iPrinted edition:
_z9783031026386
830 0 _aSynthesis Lectures on Algorithms and Software in Engineering,
_x1938-1735
_987408
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01510-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c86093
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