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007			cr nn 008mamaa
008			220601s2015 sz | s |||| 0|eng d
020			_a9783031015243
024	7		_a10.1007/978-3-031-01524-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQC427.8.S64 _b2015 EB
100	1		_aLoizou, Christos P., _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686561 _d1962-
245	1	0	_aDespeckle Filtering for Ultrasound Imaging and Video. _nVolume II, _pSelected Applications _cby Christos P. Loizou, Constantinos S. Pattichis
250			_a2nd edition 2015
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XXIV, 156 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Algorithms and Software in Engineering _x1938-1735
505	0		_aPreface -- List of Symbols -- List of Abbreviations -- Introduction and Review of Despeckle Filtering -- Segmentation of the Intima-media Complex and Plaque in CCA Ultrasound Imaging and Video Following Despeckle Filtering -- Evaluation of Despeckle Filtering of Carotid Plaque Imaging and Video Based on Texture Analysis -- Wireless Video Communication Using Despeckle Filtering and HVEC -- Summary and Future Directions -- References -- Authors' Biographies .
520			_aIn ultrasound imaging and video visual perception is hindered by speckle multiplicative noise that degrades the quality. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image/video segmentation, texture analysis and encoding in ultrasound imaging and video. The goal of the first book (book 1 of 2 books) was to introduce the problem of speckle in ultrasound image and video as well as the theoretical background, algorithmic steps, and the MatlabTM for the following group of despeckle filters: linear despeckle filtering, non-linear despeckle filtering, diffusion despeckle filtering, and wavelet despeckle filtering. The goal of this book (book 2 of 2 books) is to demonstrate the use of a comparative evaluation framework based on these despeckle filters (introduced on book 1) on cardiovascular ultrasound image and video processing and analysis. More specifically, the despeckle filtering evaluation framework is based on texture analysis, image quality evaluation metrics, and visual evaluation by experts. This framework is applied in cardiovascular ultrasound image/video processing on the tasks of segmentation and structural measurements, texture analysis for differentiating between two classes (i.e. normal vs disease) and for efficient encoding for mobile applications. It is shown that despeckle noise reduction improved segmentation and measurement (of tissue structure investigated), increased the texture feature distance between normal and abnormal tissue, improved image/video quality evaluation and perception and produced significantly lower bitrates in video encoding. Furthermore, in order to facilitate further applications we have developed in MATLABTM two different toolboxes that integrate image (IDF) and video (VDF) despeckle filtering, texture analysis, and image and video quality evaluation metrics. The code for these toolsets is open source and these are available to download complementary to the two monographs.
988			_aSynthesis Collection of Technology_2015
650		7	_2embne _9687847 _aRadar óptico
650		7	_2embne _9139972 _aDiagnóstico por imagen
650		7	_2embne _9687635 _aFiltros eléctricos digitales
700			_aPattichis, Constantinos S. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _999283
776	0	8	_iPrinted edition: _z9783031003967
776	0	8	_iPrinted edition: _z9783031026522
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01524-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI