Dr. Xiaodong Wu
at Univ of Iowa
SPIE Involvement:
Author | Instructor
Publications (11)

Proceedings Article | 13 March 2014 Paper
Proc. SPIE. 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging
KEYWORDS: Human subjects, 3D applications, Nerve, Visualization, Optical coherence tomography, Image segmentation, Retina, Error analysis, Medical imaging, Neodymium

SPIE Journal Paper | 10 July 2013
JBO Vol. 18 Issue 07
KEYWORDS: Image segmentation, Optical coherence tomography, Multilayers, Vitreous, Biological research, Eye, 3D scanning, 3D image processing, Reflectivity, Visualization

Proceedings Article | 26 March 2013 Paper
Proc. SPIE. 8567, Ophthalmic Technologies XXIII
KEYWORDS: Mathematical modeling, Eye, Nerve, Visualization, Optical coherence tomography, Stereoscopy, Image segmentation, Retina, Vitreous, 3D image processing

Proceedings Article | 13 March 2013 Paper
Proc. SPIE. 8669, Medical Imaging 2013: Image Processing
KEYWORDS: Optical spheres, Tumors, Tissues, Image segmentation, Image registration, Lung, Computed tomography, Optimization (mathematics), Positron emission tomography, 4D CT imaging

Proceedings Article | 14 February 2012 Paper
Proc. SPIE. 8314, Medical Imaging 2012: Image Processing
KEYWORDS: Optical coherence tomography, Image segmentation, Image processing, Silicon, 3D modeling, Medical imaging, Computer engineering, Optimization (mathematics), Iterative methods, 3D image processing

Showing 5 of 11 publications
Conference Committee Involvement (2)
Vision Geometry XV
1 February 2007 | San Jose, CA, United States
Vision Geometry XIV
17 January 2006 | San Jose, California, United States
Course Instructor
SC1026: Graph Algorithmic Techniques for Biomedical Image Segmentation
This course provides an in-depth overview of two state-of-the-art graph-based methods for segmenting three-dimensional structures in medical images: graph cuts and the LOGISMOS (Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces) approach. Such graph-based approaches are becoming increasingly used in the medical image analysis community, in part, due to their ability to efficiently produce globally optimal three-dimensional segmentations in a single pass (not requiring an iterative numerical scheme). Additionally, LOGISMOS enables the simultaneous optimal detection of multiple surfaces in volumetric images, which is important in many medical image segmentation applications. In the first part of the course, we provide a broad overview of both graph cuts and the LOGISMOS approach, including the presentation of a number of example applications. In the second and third parts of the course, we present the algorithmic details of graph cuts and the LOGISMOS approach, respectively. In the final part of the course, we discuss the design of cost functions, which is of paramount importance in any graph-based approach.
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