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Level set method in medical imaging segmentation / [edited by] Ayman El-Baz and Jasjit S. Suri.

Contributor(s): El-Baz, Ayman S [editor.] | Suri, Jasjit S [editor.]Material type: Text

TextPublisher: Boca Raton, FL : CRC Press, [2019]Copyright date: ©2019Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781315148595; 1315148595; 9781351373036; 135137303X; 9781351373029; 1351373021; 9781351373012; 1351373013Subject(s): Diagnostic imaging | MEDICAL / General | MEDICAL / Biotechnology | MEDICAL / Radiology & Nuclear MedicineDDC classification: 616.07/54 LOC classification: RC78.7.D53Online resources: Taylor & Francis | OCLC metadata license agreement

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.

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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.

OCLC-licensed vendor bibliographic record.