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Titlebook: Handbook of Biomedical Image Analysis; Volume 2: Segmentati Jasjit S. Suri,David L. Wilson,Swamy Laxminarayan Book 2005 Springer-Verlag US

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書(shū)目名稱(chēng)Handbook of Biomedical Image Analysis
副標(biāo)題Volume 2: Segmentati
編輯Jasjit S. Suri,David L. Wilson,Swamy Laxminarayan
視頻videohttp://file.papertrans.cn/421/420904/420904.mp4
概述First time a work is being written which covers the role of image segmentation and image registration together.Three-volume set constitutes a real system for effective use in biomedical applications t
叢書(shū)名稱(chēng)Topics in Biomedical Engineering. International Book Series
圖書(shū)封面Titlebook: Handbook of Biomedical Image Analysis; Volume 2: Segmentati Jasjit S. Suri,David L. Wilson,Swamy Laxminarayan Book 2005 Springer-Verlag US
描述.Handbook of Biomedical Image Analysis: Segmentation Models. (Volume II) is dedicated to the segmentation of complex shapes from the field of imaging sciences using different mathematicaltechniques. This volume is aimed at researchers and educators in imaging sciences, radiological imaging, clinical and diagnostic imaging, physicists covering different medical imaging modalities, as well as researchers in biomedical engineering, applied mathematics, algorithmic development, computer vision, signal processing, computer graphics and multimedia in general, both in academia and industry...Key Features:..--- Model-based brain tissue classification..--- Supervised texture classification for intravascular tissue characterization..--- Medical image segmentation: methods and applications in functional imaging..--- Automatic segmentation of pancreatic tumors in CT..--- Computerized analysis and vasodilation parameterization in flow-mediated dilation tests from ultrasonic image sequences..--- Adaptive approaches to optical segmentation in brain MRI Images..--- Automatic analysis of color fundus photographs and its application to the diagnosis of diabetic retinopathy..--- Segmentation issues o
出版日期Book 2005
關(guān)鍵詞3D; Automat; Biomedical Image Analysis; Biomedical application; Resolution; Tumor; biomedical applications
版次1
doihttps://doi.org/10.1007/b104806
isbn_ebook978-0-306-48606-7
copyrightSpringer-Verlag US 2005
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Statistical and Adaptive Approaches for Optimal Segmentation in Medical Images, increasingly popular tool in medical research and practice, especially with the increase of medical images in modality, amount, size, and dimension. Image segmentation, a process that aims at identifying and separating regions of interests from an image, is crucial in many medical applications such
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Automatic Analysis of Color Fundus Photographs and Its Application to the Diagnosis of Diabetic Ret majors role and where the practical use is evident, image processing—as a derivative of applied mathematics—is a more theoretical discipline. Hence, the conditions of this meeting need to be analyzed sophisticatedly; not everything possible to implement is useful, and not everything useful is possi
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