Surface interpolation from sparse cross sections using region correspondence

• Published in: IEEE transactions on medical imaging Vol. 19; no. 11; pp. 1106 - 1114
• Main Authors: Treece, G.M., Prager, R.W., Gee, A.H., Berman, L.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2000; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological and medical sciences; Biomedical imaging; Computed tomography; Computer Simulation; Computerized tomography; Cross sections; Data mining; Image reconstruction; Image segmentation; Interpolation; Investigative techniques, diagnostic techniques (general aspects); Magnetic resonance imaging; Materials handling; Mathematics; Medical; Medical sciences; Miscellaneous. Technology; Models, Anatomic; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Reconstruction; Reconstruction algorithms; Robustness; Segmentation; Shape; Surface reconstruction; Three dimensional displays; Ultrasonic imaging; X-ray imaging; Sonography; Human; Volumetric analysis; Radiodiagnosis; Cross section; Surface analysis; Nuclear magnetic resonance imaging; Image reconstruction; Interpolation; Sparse set; Medical imagery; Tridimensional image; Computerized axial tomography
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/42.896787

The ability to estimate a surface from a set of cross sections allows calculation of the enclosed volume and the display of the surface in three-dimensions. This process has increasingly been used to derive useful information from medical data. However, extracting the cross sections (segmenting) can be very difficult, and automatic segmentation methods are not sufficiently robust to handle all situations. Hence, it is an advantage if the surface reconstruction algorithm can work effectively on a small number of cross sections. In addition, cross sections of medical data are often quite complex. Shape-based interpolation is a simple and elegant solution to this problem, although it has known limitations when handling complex shapes. In this paper, the shape-based interpolation paradigm is extended to interpolate a surface through sparse, complex cross sections, providing a significant improvement over the authors' previously published maximal disc-guided interpolation. The performance of this algorithm is demonstrated on various types of medical data (X-ray computed tomography, magnetic resonance imaging and three-dimensional ultrasound). Although the correspondence problem in general remains unsolved, it is demonstrated that correct surfaces can be estimated from a limited amount of real data, through the use of region rather than object correspondence.