000			04061nam a22004455i 4500
999			_c387157 _d387157
001			387157
003			ES-MaUEC
005			20230208201213.0
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007			cr nn 008mamaa
008			220601s2009 sz | s |||| 0|eng d
020			_a9783031016356
024	7		_a10.1007/978-3-031-01635-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC78.7.D53 _b2009 EB
100	1		_aBanik, Shantanu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686693
245	1	0	_aLandmarking and Segmentation of 3D CT Images _cby Shantanu Banik, Rangaraj Rangayyan, Graham Boag
250			_a1st edition 2009
264		1	_aCham _bSpringer International Publishing _c2009
300			_a1 recurso en línea (XXII, 148 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Biomedical Engineering _x1930-0336
505	0		_aIntroduction to Medical Image Analysis -- Image Segmentation -- Experimental Design and Database -- Ribs, Vertebral Column, and Spinal Canal -- Delineation of the Diaphragm -- Delineation of the Pelvic Girdle -- Application of Landmarking -- Concluding Remarks.
520			_aSegmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis (CAD), treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors are difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and size. However, due to the heterogeneous tissue composition of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, segmentation of the tumor mass is a challenging problem. In this context, methods are described in this book for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images. Methods to identify and segment automatically the peripheral artifacts and tissues, the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic surface are described. Techniques are also presented to evaluate quantitatively the results of segmentation of the vertebral column, the spinal canal, the diaphragm, and the pelvic girdle by comparing with the results of independent manual segmentation performed by a radiologist. The use of the landmarks and removal of several tissues and organs are shown to assist in limiting the scope of the tumor segmentation process to the abdomen, to lead to the reduction of the false-positive error, and to improve the result of segmentation of neuroblastic tumors. Table of Contents: Introduction to Medical Image Analysis / Image Segmentation / Experimental Design and Database / Ribs, Vertebral Column, and Spinal Canal / Delineation of the Diaphragm / Delineation of the Pelvic Girdle / Application of Landmarking / Concluding Remarks.
988			_aSynthesis Collection of Technology_2009
650		7	_2embne _9140930 _aTomografía
650		7	_2embne _9413188 _aProceso digital de imágenes
650		7	_2embne _9139972 _aDiagnóstico por imagen
700	1		_aRangayyan, Rangaraj M. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686111
700	1		_aBoag, Graham S. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686694
776	0	8	_iPrinted edition: _z9783031005077
776	0	8	_iPrinted edition: _z9783031027635
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01635-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI