000 04212nam a22005295i 4500
001 978-3-031-01635-6
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005 20240730163643.0
007 cr nn 008mamaa
008 220601s2009 sz | s |||| 0|eng d
020 _a9783031016356
_9978-3-031-01635-6
024 7 _a10.1007/978-3-031-01635-6
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
_2thema
082 0 4 _a620
_223
100 1 _aBanik, Shantanu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979748
245 1 0 _aLandmarking and Segmentation of 3D CT Images
_h[electronic resource] /
_cby Shantanu Banik, Rangaraj Rangayyan, Graham Boag.
250 _a1st ed. 2009.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2009.
300 _aXXII, 148 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 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.
650 0 _aEngineering.
_99405
650 0 _aBiophysics.
_94093
650 0 _aBiomedical engineering.
_93292
650 1 4 _aTechnology and Engineering.
_979749
650 2 4 _aBiophysics.
_94093
650 2 4 _aBiomedical Engineering and Bioengineering.
_931842
700 1 _aRangayyan, Rangaraj.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979750
700 1 _aBoag, Graham.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979751
710 2 _aSpringerLink (Online service)
_979752
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031005077
776 0 8 _iPrinted edition:
_z9783031027635
830 0 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
_979753
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01635-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c84840
_d84840