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12 - Digital image processing: an overview

Published online by Cambridge University Press:  01 March 2011

R. Nick Bryan
Affiliation:
University of Pennsylvania
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Summary

Characteristics of digital image data

Images produced by most of the current biomedical imaging systems, right from the signal capture and transduction level, are digital in nature. The main purpose of processing such images is to produce qualitative and quantitative information about an object/object system under study by using a digital computer, given multiple, multimodality biomedical images pertaining to the object system under study. As described in other chapters, currently many imaging modalities are available that capture morphological (anatomical), physiological, and molecular information about the object system being studied. The types of objects studied may include rigid (e.g., bones), deformable (e.g., soft-tissue structures), static (e.g., skull), dynamic (e.g., lungs, heart, joints), and conceptual (e.g., activity regions in PET and functional MRI, isodose surfaces in radiation therapy) objects.

Currently, two- and three-dimensional (2D and 3D) images are ubiquitous in biomedicine, e.g., a digital or digitized radiograph (2D), and a volume of tomographic slices of a static object (3D). Four-dimensional (4D) images are also becoming available which may be thought of as comprising sequences of 3D images representing a dynamic object system, e.g., a sequence of 3D CT images of the thorax representing a sufficient number of time points of the cardiac cycle. In most applications, the object system of study consists of several static objects. For example, an MRI 3D study of a patient's head may focus on three 3D objects: white matter, gray matter, and cerebrospinal fluid (CSF).

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Publisher: Cambridge University Press
Print publication year: 2009

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References

Udupa, JK, Herman, GT, eds. 3D Imaging in Medicine, 2nd edn. Boca Raton, FL: CRC Press, 2000.
Raya, SP, Udupa, JK. Shape-based interpolation of multidimensional objects. IEEE Trans Med Imag 1990; 9: 32–42.CrossRefGoogle ScholarPubMed
Grevera, GJ, Udupa, JK. Shape-based interpolation of multidimensional grey-level images. IEEE Trans Med Imag 1996; 15: 882–92.CrossRefGoogle ScholarPubMed
Goshtasby, A, Turner, DA, Ackerman, LV. Matching tomographic slices for interpolation. IEEE Trans Med Imag 1992; 11: 507–6.CrossRefGoogle ScholarPubMed
Gerig, G, Kübler, O, Kikinis, R, Jolesz, FA. Nonlinear anisotropic filtering of MRI data. IEEE Trans Med Imag 1992; 11: 221–32.CrossRefGoogle ScholarPubMed
Sonka, M, Hlavac, V, Boyle, R. Image Processing, Analysis, and Machine Vision, 2nd edn. Pacific Grove, CA: Brooks/Cole, 1999.Google Scholar
Gong, L, Kulikowski, C. Comparison of image analysis processes through object-centered hierarchical planning. IEEE Trans PAMI 1995; 17: 997–1008.CrossRefGoogle Scholar
Christensen, GE, Rabbitt, R, Miller, MI. 3-D brain mapping using a deformable neuroanatomy. Phys Med Biol 1994; 39: 609–18.CrossRefGoogle Scholar
Cootes, TF, Taylor, CJ, Cooper, DH, Graham, J. Active shape models: their training and application. Comput Vision Imag Und 1995; 61: 38–59.CrossRefGoogle Scholar
Doyle, W. Operations useful for similarity-invariant pattern recognition, J ACM 1962; 9: 259–67.CrossRefGoogle Scholar
Udupa, JK, Srihari, S, Herman, GT. Boundary detection in multidimensions. IEEE Trans PAMI 1982; 4: 41–50.CrossRefGoogle ScholarPubMed
Lorensen, W, Cline, H. Marching cubes: a high resolution 3D surface construction algorithm. Comput Graph 1989; 23: 185–94.Google Scholar
Levoy, M. Display of surfaces from volume data. IEEE Comput Graph Appl 1988; 8: 29–37.CrossRefGoogle Scholar
Herman, GT, Liu, HK. Dynamic boundary surface detection. Comput Graph Image Proc 1978; 7: 130–8.CrossRefGoogle Scholar
Kass, M, Witkin, A, Terzopoulous, D. Snakes: active contour models. Int J Comput Vision 1987; 1: 321–31.CrossRefGoogle Scholar
Cohen, I, Cohen, LD, Ayache, N. Using deformable surfaces to segment 3-D images and infer differential structures. CVGIP Image Understand 1992; 56: 242–63.CrossRefGoogle Scholar
Falcao, A, Udupa, JK, Samarasekera, S, et al. User-steered image segmentation paradigms: live wire and live lane. Graph Mod Imag Proc 1998; 60: 233–60.CrossRefGoogle Scholar
Duda, RO, Hart, PE, Stork, DG. Pattern Classification. New York, NY: Wiley, 2001.Google Scholar
Drebin, R, Carpenter, L, Hanrahan, P. Volume rendering. Comput Graph 1988; 22: 65–74.CrossRefGoogle Scholar
Udupa, J K, Odhner, D, Samarasekera, S, et al. 3DVIEWNIX: an open transportable, multidimensional, multimodality, multiparametric imaging software system. SPIE Proc 1994; 2164: 58–73.CrossRefGoogle Scholar
Udupa, JK, Saha, PK. Fuzzy connectedness in image segmentation. Proce IEEE 2003; 91: 1649–99.CrossRefGoogle Scholar
Chu, S, Yuille, A. Region competition: unifying snakes, region growing and Bayes/MDL for multi-band image segmentation. IEEE Trans PAMI 1996; 18: 884–900.Google Scholar
Napel, S, Marks, MP, Rubin, GD, et al. CT angiography with spiral CT and maximum intensity projection. Radiology 1992; 185: 607–10.CrossRefGoogle ScholarPubMed
Goldwasser, S, Reynolds, R. Real-time display and manipulation of 3-D medical objects: the voxel machine architecture. Comput Vision Graph Imag Proc 1987; 39: 1–27.CrossRefGoogle Scholar
Levoy, M. Display of surfaces from volume data. ACM Trans Graph 1990; 9: 245–71.CrossRefGoogle Scholar
Herman, GT, Udupa, JK. Display of 3-D information in 3-D digital images: computational foundations and medical application. IEEE Comput Graph Appl 1983; 3: 39–46.CrossRefGoogle Scholar

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