| Description |
1 online resource |
| Contents |
Tomography reconstructions with stochastic level-set methods / Bruno Sixou, Lin Wang, and Francoise Peyrin -- Application of 3D level set based optimization in microwave breast imaging for cancer detection / Hardik N. Patel and Deepak K. Ghodgaonkar -- A modified global and elastic ICP shape registration for medical imaging applications / Hossam Abd El Munim and Aly A. Farag -- Robust nuclei segmentation using statistical level set method with topology preserving constraint / Shaghayegh Taheri, Thomas Fevens, and Tien D. Bui -- Level set methods in segmentation of SDOCT retinal images / Padmasini N, Umamaheswari R, Mohamed Yacin Sikkandar and Manavi D Sindal -- Numerical techniques for level set models : an image segmentation perspective / Elisabetta Carlini, Maurizio Falcone, and Roberto Ferretti -- Level set methods for cardiac segmentation in MSCT images / Ruben Medina, Sebastian Bautista, Villie Morocho, and Alexandra La Cruz -- Deformable models and image segmentation / Ahmed ElTanboly, Ali Mahmoud, Ahmed Shalaby, Magdi El-Azab, Mohammed Ghazal, Robert Keynton, and Ayman El-Baz -- Cardiac image segmentation using generalized polynomial chaos expansion and level set function / Yuncheng Du, and Dongping Du -- Medical image segmentation approach that uses level sets with statistical shape priors / Ahmed Eltanboly, Mohammed Ghazal, Hassan Hajjdiab, Ali Mahmoud, Ahmed Shalaby, Jasjit Suri, Robert Keynton, and Ayman El-Baz -- Level set method in medical imaging segmentation / Jiangxiong Fang -- Image segmentation with B-spline level set / Shenhai Zheng, Bin Fang, and Laquan Li |
| Summary |
Level set methods are numerical techniques which offer remarkably powerful tools for understanding, analyzing, and computing interface motion in a host of settings. When used for medical imaging analysis and segmentation, the function assigns a label to each pixel or voxel and optimality is defined based on desired imaging properties. This often includes a detection step to extract specific objects via segmentation. This allows for the segmentation and analysis problem to be formulated and solved in a principled way based on well-established mathematical theories. Level set method is a great tool for modeling time varying medical images and enhancement of numerical computations |
| Bibliography |
Includes bibliographical references |
| Notes |
Ayman El-Baz is a University Scholar and Chair, Bioengineering Department at the University of Louisville, KY. He has over 15 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnostic systems. He has developed new techniques for the accurate identification of probability mixtures for segmenting multi-modal images, new probability models, and model-based algorithms for recognizing lung nodules and blood vessels in magnetic resonance and computer tomography imaging systems, as well as new registration techniques based on multiple second-order signal statistics, all of which have been reported at multiple international conferences and journal articles. His work related to novel image analysis techniques for autism, dyslexia, and lung cancer has earned multiple awards including the Walter H. Coulter Foundation Early Career in Biomedical Engineering and a Research Scholar Grant from the American Cancer Society. He has authored or coauthored more than 300 technical articles (87 journals, 9 books, 39 book chapters, 144 refereed-conference papers, 74 abstracts published in proceedings and 12 US patents). Jasjit S. Suri is Chairman of Global Biomedical Technologies, Inc. Roseville, CA. He has spent over 30 years in the fields of biomedical engineering/sciences, software and hardware engineering and its management. He has developed products and worked extensively in the areas of breast, mammography, orthopedics (spine), neurology (brain), angiography, urology and image guided surgery. Dr. Suri has over 100 US/European Patents, 20 Trademarks, 35 books and over 550 peer reviewed articles. He is a Fellow of AIMBE (American Institute of Medical and Biological Engineering) |
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Online resource; title from PDF title page (EBSCO, viewed July 1, 2019) |
| Subject |
Diagnostic imaging.
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Image Interpretation, Computer-Assisted -- methods
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Image Processing, Computer-Assisted -- methods
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Mathematical Computing
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Diagnostic Imaging
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MEDICAL -- General.
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MEDICAL -- Biotechnology.
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MEDICAL -- Radiology & Nuclear Medicine.
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Diagnostic imaging
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| Form |
Electronic book
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| Author |
El-Baz, Ayman S., editor.
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Suri, Jasjit S., editor.
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| ISBN |
9781315148595 |
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1315148595 |
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9781351373036 |
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135137303X |
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9781351373029 |
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1351373021 |
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9781351373012 |
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1351373013 |
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