Id Zo Id Jo

maga r'j

FIGURE 10 Image segmentation using correlation mapping. (A) First image in a sequence of 60 temporal images with 3x3 pixel ROIs drawn in tumor and normal area; (B) plot of the average intensity of the reference ROI (tumor) and the normal ROI for 60 images in a sequence; (C) correlation map of the tumor See also Plate 5.

segmentation and visualization tools for temporal sequences of images. They are particularly useful for evaluation of disease processes, drug treatments, or radiotheraphy results.

7 Other Techniques

Combined (hybrid) strategies have also been used in many applications. Here are some examples: Kapur etal. [58] present a method for segmentation of brain tissue from magnetic resonance images that combines the strengths of three techniques: single-channel expectation/maximization segmentation, binary mathematical morphology, and active contours models. Masutani et al. [73] segment cerebral blood vessels on MRA images using a model-based region growing, controlled by morphological information of local shape. A hybrid strategy [3] that employs image processing techniques based on anisotropic filters, thresholding, active contours, and a priori knowledge of the segmentation of the brain is discussed in Chapter 11.

Many segmentation techniques developed originally for two-

dimensional images can be extended to three dimensions — for example, region growing, edge detection, or multispectral segmentation [12,19,21,90,125]. 3D segmentation combined with 3D rendering allows for more comprehensive and detailed analysis of image structures than is possible in a spatially limited single-image study. A number of 3D segmentation techniques can be found in the literature, such as 3D connectivity algorithm with morphological interpolation [57], 3D matching of deformable models [70], 3D edge detection [78], coupled surfaces propagation using level set methods [131], and a hybrid algorithm based on thresholding, morphological operators, and connected component labeling [46,100]. Several volumetric segmentation algorithms are discussed in Chapter 12, where their accuracy is compared using digital MR phantoms. Partial volume segmentation with voxel histograms is presented in Chapter 13.

There has been great interest in building digital volumetric models (3D atlases) that can be used as templates, mostly for the MR segmentation of the human brain [23,47,61]. A modelbased segmentation is achieved by using atlas information to guide segmentation algorithms. In the first step, a linear registration is determined for global alignment of the atlas with the image data. The linear registration establishes corresponding regions and accounts for translation, rotation and scale differences. Next, a nonlinear transform (such as elastic warping, [5]) is applied to maximize the similarity of these regions.

Warfield et al. [122,123] developed a new, adaptive, template-moderated, spatially varying, statistical classification algorithm. The algorithm iterates between a classification step to identify tissues and an elastic matching step to align a template of normal anatomy with the classified tissues. Statistical classification based upon image intensities has often been used to segment major tissue types. Elastic registration can generate a segmentation by matching an anatomical atlas to a patient scan. These two segmentation approaches are often complementary. Adaptive, template moderated, spatially varying, statistical classification integrates these approaches, avoiding many of the disadvantages of each technique alone, while exploiting the combination. The algorithm was applied to several segmentation problems, such as quantification of normal anatomy (MR images of brain and knee cartilage) and pathology of various types (multiple sclerosis, brain tumors, and damaged knee cartilage). In each case, the new algorithm provided a better segmentation than statistical classification or elastic matching alone.

Figure 11 shows an example of 3D segmentation of normal and pathological brain tissues. The tumor segmentation was carried out with the algorithm of Kaus et al. [60]. This visualization was used to support preoperative surgical planning for tumor resection.

In some medical images, regions that have similar average intensities are visually distinguishable because they have different textures. In such cases, the local texture can be

FIGURE 11 Rendering of 3D anatomical models and 2D MRI cross-sections of a patient with a meningioma. The models of the skin surface, the brain, and the tumor (green) are based on automatically segmented 3D MRI data. The precentral gyrus (yellow) and the corticospinal tract (blue) are based on a previously aligned digital brain atlas [61]. See also Plate 6. (Courtesy of Drs. Ron Kikinis, Michael Kaus, and Simon Warfield, Surgical Planning Lab, Department of Radiology, Brigham and Women's Hospital, Boston.)

FIGURE 11 Rendering of 3D anatomical models and 2D MRI cross-sections of a patient with a meningioma. The models of the skin surface, the brain, and the tumor (green) are based on automatically segmented 3D MRI data. The precentral gyrus (yellow) and the corticospinal tract (blue) are based on a previously aligned digital brain atlas [61]. See also Plate 6. (Courtesy of Drs. Ron Kikinis, Michael Kaus, and Simon Warfield, Surgical Planning Lab, Department of Radiology, Brigham and Women's Hospital, Boston.)

quantified using techniques described in Chapters 14 and 15. Each pixel can be assigned a texture value and the image can be segmented using texture instead of intensity [6,79].

Fuzzy clustering, which provides another approach for segmentation of two-dimensional or multispectral images, is discussed in Chapter 6. Segmentation has also been addressed with neural networks in several applications [2,28,37,40,69,82,101,132]. The use of neural networks for segmentation is illustrated in Chapter 7. The family of active contour (snakes, deformable templates) algorithms that have been widely used for medical image segmentation [74, 95, 128, 129] is presented in Chapter 8, shape constraints for deformable models are discussed in Chapter 9 and gradient vector flow deformable models are explained in Chapter 10.

8 Concluding Remarks

Segmentation is an important step in many medical applications involving measurements, 3D visualization, registration, and computer-aided diagnosis. This chapter was a brief introduction to the fundamental concepts of segmentation and methods that are commonly used.

Selection of the "correct" technique for a given application is a difficult task. Careful definition of the goals of segmentation is a must. In many cases, a combination of several techniques may be necessary to obtain the segmentation goal. Very often integration of information from many images (acquired from different modalities or over time) helps to segment structures that otherwise could not be detected on single images.

As new and more sophisticated techniques are being developed, there is a need for objective evaluation and quantitative testing procedures [17,20,26]. Evaluation of segmentation algorithms using standardized protocols will be useful for selection of methods for a particular clinical application.

Clinical acceptance of segmentation techniques depends also on ease of computation and limited user supervision. With the continued increases in computer power, the automated realtime segmentation of multispectral and multidimensional images will become a common tool in clinical applications.


1. R. Adams, L. Bischof, "Seeded region growing," IEEE Trans. Pattern Recogn. Mach. Intell, Vol. 16, no. 6, pp. 641-647, 1994.

2. S. C. Amartur, D. Piraino, Y. Takefuji, "Optimization neural networks for the segmentation of Magnetic Resonance images," IEEE Trans. Med. Imag., vol. 11, no. 2, pp. 215-220, 1992.

3. M. S. Atkins, B. T. Mackiewich, "Fully automatic segmentation of the brain in MRI," IEEE Trans. Med. Imag., vol. 17, no. 1, pp. 98-107, 1998.

4. G. J. Awcock, R. Thomas, Applied Image Processing. New York: McGraw-Hill, Inc., 1996.

5. R. Bajcsy, S. Kovacic, "Multiresolution elastic matching," Comp. Vision, Graphic and Image Proc., vol. 46, pp. 1-21, 1989.

6. D. G. Ballard, C. M. Brown, Computer Vision, Englewood Cliffs: Prentice Hall, 1982.

7. P. A. Bandettini, A. Jesmanowicz, E. C. Wong, J. S. Hyde, "Processing strategies for functional MRI of the human brain," Magn. Res. Med., vol. 30, pp. 161-173, 1993.

8. I. N. Bankman, T. Nizialek, I. Simon, O. B. Gatewood, I. N. Weinberg, W. R. Brody, "Segmentation algorithms for detecting microcalcifications in mammograms," IEEE Trans. Inform. Techn. Biomed., vol. 1, no. 2, pp. 141-149.

9. J. Bernsen, "Dynamic thresholding of gray-level images," in Proc. 8th Int. Conf. Pattern Recognition, Paris, France, pp. 1251-55, Oct. 1986.

10. S. Beucher, "Segmentation tools in mathematical morphology," SPIE, vol. 1350, Image Algebra and Morphological Image Processing, pp. 70-84, 1990.

11. J. C. Bezdek, L.O. Hall, L. P. Clarke, "Review of MR image segmentation techniques using pattern recognition," Med. Phys, vol. 20, no. 4, pp. 1033-1048, 1993.

12. M. Bomans, K.-H. Hohne, U. Tiede, M. Riemer, "3-D segmentation of MR images of the head for 3-D display," IEEE Trans. Medical Imaging, vol. 9, no. 2, pp. 177-183, 1990.

13. M. E. Bramdt, T. P. Bohan, L. A. Kramer, J. M. Fletcher, "Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR imaging," Computerized Med. Imag. Graphics, vol. 18, no.1, pp. 25-34, 1994.

14. J. F. Brenner, J. M. Lester, W. D. Selles, "Scene segmentation in automated histopathology: techniques evolved from cytology automation," Pattern Recognition, vol. 13, pp. 65-77, 1981.

15. D. Brzakovic, X. M. Luo, P. Brzakovic, "An approach to automated detection of tumors in mammograms," IEEE Trans. Med. Imag., vol. 9, no. 3, pp. 233-241, 1990.

16. K. R. Castleman, Digital Image Processing. Upper Saddle River: Prentice Hall, 1996.

17. V. Chalana, Y. Kim, "A methodology for evaluation of boundary detection algorithms on medical images," IEEE Med.. Imag., vol. 16, no. 5, pp. 634-652, 1997.

18. C. K. Chow, T. Kaneko, "Automatic boundary detection of the left ventricle from cineangiograms," Comput. Biomed. Res., vol. 5, pp. 388-410, 1972.

19. L. P. Clarke, R. P. Velthuizen, S. Phuphanich, J. D. Schellenberg, J. A. Arrington, M. Silbiger, "MRI: Stability of three supervised segmentation methods," Mag. Res. Imag., vol. 11, pp. 95-106, 1993.

20. L. P. Clarke, R. P. Velthuizen, M. A. Camacho, J.J. Heine, M. Vaidyanathan, L. O. Hall, R.W. Thatcher, M.L. Silbiger, "MRI segmentation: methods and applications," Mag. Res. Imag., vol. 13, no. 3, pp. 334-368, 1995.

21. H. E. Cline, C. L. Dumoulin, H. R. Hart, W. E. Lorensen, S. Ludke, "3D reconstruction of the brain from magnetic resonance images using a connectivity algorithm," Magn. Reson. Imaging, vol. 5, pp. 345-352, 1987.

22. H. E. Cline, W. E. Lorensen, R. Kikinis, R. Jolesz, "Three-dimensional segmentation of MR images of the head using probability and connectivity," J Comp. Assisted Tomography, vol. 14, no. 6, pp. 1037-1045, 1990.

23. D. L. Collins, T. M. Peters, W. Dai, A. C. Evans, "Model based segmentation of individual brain structures from MRI data," In: R. A. Robb, Ed.: Visualization in Biomedical Computing II, Proc. SPIE 1808. Chapel Hill, NC, pp. 10-23, 1992.

24. L. S. Davies, "A survey of edge detection techniques," Computer Graph. andImageProc.,vol. 4, pp. 248-270,1975.

25. E. R. Davies, Machine Vision, San Diego: Academic Press, 1997.

26. C. deGraaf, A. Koster, K. Vincken, M. Viergever, "A methodology for the validation of image segmentation algorithms," In: Proc. IEEE Symp. Computer-Based Medical Systems, pp. 17-24, 1992.

26a. A. P. Dempster, N. M. Laird, D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc., vol. 39, pp. 1-38, 1977.

27. R.O. Duda, P. E. Hart, Pattern Recognition and Scene Analysis, New York: Wiley, 1973.

28. S. Fahlman, G. Hinton, "Connectionist architectures for artificial intelligence," IEEE Computer, vol. 20, no. 1, pp. 100-109, 1987.

29. L. M. Fletcher, J. B. Marsotti, J. P. Hornak, "A multispectral analysis of brain tissues," Magn. Reson. Med., vol. 29, pp. 623-630, 1993.

30. K. S. Fu, J. K. Mui, "A survey on image segmentation," Pattern Recognition, vol. 13, no. 1, pp. 3-16, 1981.

31. G. Gerig, J. Martin, R. Kikinis, O. Kuebler, M. Shenton, F. A. Jolesz, "Automating segmentation of dual-echo MR head data," In: Information Processing in Medical Imaging A. C. Colchester, D. J. Hawkes, Eds., Berlin: SpringerVerlag, pp. 175-185, 1991.

32. G. Gerig, O. Kubler, R. Kikinis, F. A. Jolesz, "Nonlinear anisotropic filtering of MRI data," IEEE Trans Med. Imag., vol. 11, no. 2, pp. 221-232, 1992.

33. G. Gerig, D. Welti, C. Guttmann, A. Colchester, G. Szekely, "Exploring the discrimination power of the time domain for segmentation and characterization of lesions in serial MR data," Proc. of First Intern. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'98), pp. 469-479, in Lecture Notes in Computer Science, Eds. W. M. Wells, A. Colchester, and S. Delp, Springer Verlag, 1998.

34. S. Gilles, M. Brady, J. P. Thirion, N. Ayache, "Bias field correction and segmentation of MR images," Proc. VBC '96, Visualization in Biomedical Computing, in Lecture Notes in Computer Science, vol. 1131, pp. 153-158, Springer-Verlag, 1996.

35. J. E. Golston, R. H. Moss, W. V. Stoecker, "Boundary detection in skin tumor images: An overall approach and a radial search algorithm," Pattern Recognition, vol. 23, no. 11, pp. 1235-1247, 1990.

36. R. C. Gonzalez, R. E. Woods, Digital Image Processing. Reading, MA: Addison-Wesley Publishing Company, 1993.

37. E. Gose, R. Johnsonbaugh, S. Jost, Pattern Recognition and Image Analysis. Upper Saddle River: Prentice Hall, 1996.

38. A. Goshtasby, D. A. Turner, "Segmentation of Cardiac Cine MR Images for extraction of right and left ventricular chambers," IEEE Trans. Med. Imag., vol. 14, no. 1, pp. 56-64, 1995.

39. R. Guillemaud, M. Brady, "Estimating the bias field of MR images," IEEE Trans. Med. Imag., vol. 16, no. 3, pp. 238-251, 1997.

40. L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger, J. C. Bezdek, "A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain," IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 672-682, 1992.

41. R. M. Haralick, L. G. Shapiro, "Survey: image segmentation techniques," Comp. Vision Graph Image Proc., vol. 29, pp. 100-132, 1985.

41a. J. J. Heine, S. R. Deans, D. K. Cullers, R. Stauduhar, L. P. Clarke, "Multiresolution statistical analysis of highresolution digital mammograms," IEEE Trans. Med. Imag., vol. 16, no. 5, pp. 503-515, 1997.

42. K. Held, E. Rota Kopps, B. Krause, W. Wells, R. Kikinis, H. Muller-Gartner, "Markov random field segmentation of brain MR images," IEEE Trans. Med. Imag., vol. 16, pp. 878-887, 1998.

43. W. E. Higgins, E. J. Ojard, "Interactive morphological watershed analysis for 3D medical images," Proceedings of the Symposium on 3D Advanced Image Processing in Medicine, Rennes, France, pp. 117-121, Nov. 2-4, 1992.

44. K. H. Höhne, M. Bohm M, G. C. Nicolae, "The processing of X-ray image sequences," In: P. Stucki, Ed., Advances in Digital Image Processing. New York: Plenum Press, pp. 147-163, 1979.

45. K. H. Höhne, M. Bohm, "Processing and analysis of radiographic image sequences," In: TS Huang, Ed., Image Sequence Processing and Dynamic Scene Analysis. Berlin: Springer-Verlag (NATO ASI Series, Vol F2), pp. 602-623, 1983.

46. K. H. Höhne, W. A. Hanson, "Interactive 3D-segmenta-tion of MRI and CT volumes using morphological operations," J. Comput. Assist. Tomogr., vol. 16, no. 2, pp. 285-294, 1992.

47. K. H. Höhne, M. Bomans, M. Riemer, R. Schubert, U. Tiede, W. Lierse, "A 3D anatomical atlas based on a volume model," IEEE Comput. Graphics Appl., vol. 12, pp. 72-78, 1992.

48. X. Hu, N. Alperin, D. N. Levin, K. K. Tan, M. Mengeot, "Visualization of MR angiography data with segmentation and volume-rendering techniques," J. Magn. Res. Imag., vol. 1, pp. 539-546, 1991.

49. X. Hu, V. Johnson, W. H. Wong, C. T. Chen, "Bayesean image processing in magnetic resonance imaging," Magn. Reson. Imag., vol. 9, pp. 611-620, 1991.

50. H. Itagaki, "Improvements of nuclear magnetic resonance image quality using iterations of adaptive nonlinear filtering," IEEE Trans. Med. Imag., vol. 12, no. 2, pp. 322-327, 1993.

51. B. Jahne, Practical Handbook on Image Processing for Scientific Applications, Boca Raton, FL: CRC Press, 1997.

52. R. Jain, R. Kasturi, B. G. Schunck, Machine Vision, New York: McGraw-Hill, 1995.

53. A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice Hall, 1989.

54. A. K. Jain, P. J. Flynn, "Image segmentation using clustering," in Advances in Image Understanding, K. Bowyer, and N. Ahuja, Eds. Los Alamitas, CA: IEEE Computer Society Press, 1996.

55. E. F. Jackson, P. A. Narayana, J. S. Wolinsky, T. J. Doyle, "Accuracy and reproducibility in volumetric analysis of multiple sclerosis lesions," J Comput. Assist. Tomogr., vol. 17, pp. 200-205, 1993.

56. L. A. Johnson, J. D. Pearlman, C. A. Miller, T. I. Young, K. R. Thulborn, "MR quantification of cerebral ventricular volume using a semiautomated algorithm," AJNR, vol. 14, pp. 1313-1378, 1993.

57. M. Joliot, B. M. Mazoyer, "Three-dimensional segmentation and interpolation of magnetic resonance brain images," IEEE Trans. Med. Imag., vol. 12, no. 2, pp. 269-277, 1993.

58. T. Kapur, W. E. L. Grimson, W. M. Wells, R. Kikinis, "Segmentation of brain tissue from magnetic resonance images," Medical Image Analysis, vol. 1, no. 2, pp. 109127, 1996.

59. T. Kapur, W. E. L. Grimson, R. Kikinis, W. M. Wells, "Enhanced spatial priors for segmentation of magnetic resonance imagery," Proc. of First Int. Conf on Medical Image Computing and ComputerAssisted Intervention (MICCAI'98), pp. 457-468, in Lecture Notes in Computer Science, Eds. W. M. Wells, A. Colchester, and S. Delp, Springer Verlag, 1998.

60. M. R. Kaus, S. K. Warfield, A. Nabavi, E. Chatzidakis, P. M. Black, F. A. Jolesz, R. Kikinis, "Segmentation of meningiomas and low grade gliomas in MRI," Proc. of Second Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'99), pp. 1-10, in Lecture Notes in Computer Science, Eds. C. Taylor and A. Colchester, Springer Verlag, 1999.

61. R. Kikinis, M. E. Shenton, D. V. Iosifescu, R. W. McCarley, P. Saiviroonporn, H. H. Hokama, A. Robatino, D. Metcalf, C. G. Wible, C. M. Portas, R. Donnino, F. A. Jolesz, "Digital brain atlas for surgical planning, model driven segmentation and teaching," IEEE Trans. Visualiz. and Comp. Graph., vol. 2, no. 3, 1996.

62. J. Kittler, J. Illingworth, J. Foglein, "Threshold based on a simple image statistics," Comput. Vision Graph. Image Process., vol. 30, pp. 125-147, 1985.

63. A. Kundu, "Local segmentation of biomedical images," Comput. Med. Imaging Graph., vol. 14, pp. 173-183, 1990.

64. L. M. Lester, H. A. Williams, B. A. Weintraub, J. F. Brenner, "Two graph searching techniques for boundary finding in white blood cell images," Comput. Biol. Med., vol. 8, pp. 293-308, 1978.

65. J. M. Lester, J. F. Brenner, W. D. Selles, "Local transforms for biomedical image analysis," Comp. Graph. Imag. Proc., vol. 13, pp. 17-30, 1980.

66. Z. Liang, "Tissue classification and segmentation of MR images,'' IEEE in Medicine and Biology, vol. 12, no. 1, pp. 81-85, 1993.

67. K. Lim, A. Pfefferbaum, "Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter,'' J. Comput. Assist. Tomogr., vol. 13, pp. 588-593, 1989.

68. J. S. Lim, Two-Dimensional Signal and Image Processing. Englewood Cliffs: Prentice Hall, 1990.

69. W. C. Lin, E. C. Tsao, C. T. Chen, "Constraint satisfaction neural networks for image segmentation,'' Pattern Recogn., vol. 25, no. 7, pp. 679-693, 1992.

70. J. Lotjonen, I. E. Mangin, P-J. Reissman, J. Nenonen, T. Katila, "Segmentation of magnetic resonance images using 3D deformable models,'' Proc. of First Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'98), pp. 1213-1221, in Lecture Notes in Computer Science, W.M. Wells, A. Colchester, and S. Delp, Eds. Springer Verlag, 1998.

71. F. A. Lucas-Quesada, U. Sinha, S. Sinha, "Segmentation strategies for breast tumors from dynamic MR images,'' JMRI, vol. 6, pp. 753-763, 1996.

72. D. Marr, E. Hildreth, "Theory of edge detection,'' Proc. Roy. Soc. London, vol. 27, pp. 187-217, 1980.

73. Y. Masutani. T. Schiemann, K-H. Hohne, "Vascular shape segmentation and structure extraction using a shape-based region-growing model,'' Proc. of First Int. Conf. on Medical Image Computing and ComputerAssisted Intervention (MICCAI'98), pp. 1242-1249, in Lecture Notes in Computer Science, W. M. Wells, A. Colchester, and S. Delp, Eds. Springer Verlag, 1998.

74. T. McInerney, D. Terzopoulos, "Deformable models in medical image analysis: a survey,'' Medical Image Analysis, vol. 1, no. 2, pp. 91-108, 1996.

75. F. Meyer, S. Beucher, "Morphological segmentation,'' Journal of Visual Communication and Image Representation, vol. 1(1), pp. 21-46, 1990.

76. J. R. Mitchell, S. J. Karlik, D. H. Lee, A. Fenster, "Computer-assisted identification and quantification of multiple sclerosis lesions in MR imaging volumes in the brain,'' JMRI, vol. 4, pp. 197-208, 1994.

77. A. Mitiche, J. K. Aggarwal, "Image segmentation by conventional and information-integrating techniques: a synopsis,'' Image and Vision Computing, vol. 3, no. 2, pp. 50-62, 1985.

78. O. Monga, N. Ayache, P. T. Sander, "From voxel to intrinsic surface features,'' Image and Vision Computing, vol. 10, no. 6, pp. 403-417, 1992.

79. R. Muzzolini, Y-H. Yang, R. Pierson, "Multiresolution texture segmentation with application to diagnostic ultrasound images,'' IEEE Trans. on Med. Imag., vol. 12, no. 1, pp. 108-123, 1993.

79a. K. Nagata, T. Asano, Functional image of dynamic computed tomography for the evaluation of cerebral hemodynamics, Stroke, vol. 21, pp. 882-889, 1990.

80. Y. Nakagawa, A. Rosenfeld, "Some experiments on variable thresholding," Pattern recognition, vol. 11, pp. 191-204, 1979.

81. N. J. Nilsson, Problem Solving Methods in Artificial Intelligence. New York: McGraw-Hill, 1971.

82. M. Ozkan, B. M. Dawant, R. J. Maciunas, "Neural-network based segmentation of multi-modal medical images: A comparative and prospective study," IEEE Trans Med. Imag., vol. 12, no. 3, pp. 534-544, 1993.

83. N. R. Pal, S. K. Pal, "A review on image segmentation techniques," Pattern Recognition, vol. 26, no. 9, pp. 12271249, 1993.

84. T. Pavlidis, Structural Pattern Recognition, Berlin: Springer-Verlag, 1977.

85. W. K. Pratt, Digital Image Processing. New York: John Wiley & Sons, 1991.

86. K. Preston, M. J. Duff, Modern Cellular Automata. New York: Plenum Press, 1984.

87. J. C. Rajapakse, J. N. Giedd, J. L. Rapoport, "Statistical approach to segmentation of single-channel cerebral MR images," IEEE Trans. Med. Imag., vol. 16, no. 2, pp. 176186, 1997.

88. J. C. Rajapakse, J. N. Giedd, C. DeCarli, J. W. Snell, A. McLaughlin, Y.C. Vauss, A.L. Krain, S.D. Hamburger, J. L. Rapoport, "A technique for singlechannel MR brain tissue segmentation: Application to a pediatric study," Magn. Reson. Imag., vol. 18, no. 4, 1996.

89. W. E. Reddick, J. O. Glass, E. N. Cook, T. D. Elkin, R. J. Deaton, "Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks," IEEE Trans. Med. Imag., vol. 16, no. 6, pp. 911-918, 1997.

89a. G. Riediger, L. M. Gravinghoff, K. H. Hohne, E. W. Keck, Digital cine angiographic evaluation of pulmonary blood flow velocity in ventricular septal defect. Cardiovasc. Intervent. Radiol, vol. 11, pp. 1-4, 1988.

90. R. Robb, Three-dimensional Biomedical Imaging. New York: VCH Publishers, Inc., 1995.

91. J. Rogowska, "Similarity methods for dynamic image analysis," Proceedings of International Conf. ofAMSE, vol. 2, pp. 113-124, 1991.

92. J. Rogowska, G. L. Wolf, "Temporal correlation images derived from sequential MRI scans," J. Comp. Assist. Tomogr., vol. 16, no. 5, pp. 784-788, 1992.

93. J. Rogowska, K. Preston, G. J. Hunter, L. M. Hamberg, K. K. Kwong, O. Salonen, G. L. Wolf, "Applications of similarity mapping in dynamic MRI," IEEE Trans. on Med. Imag., vol. 14, no. 3, pp. 480-486, 1995.

94. J. Rogowska, B. Hoop, P. Bogorodzki, J. A. Cannillo, G. L. Wolf, "Delay time mapping of renal function with slip-ring CT," Proc. of the RSNA Scientific Assembly and Annual Meeting, Chicago, IL, Nov. 26-Dec. 1, 1995.

95. J. Rogowska, K. Batchelder, G. S. Gazelle, E. F. Halpern, W. Connor, G. L. Wolf, "Quantitative CT lymphography: evaluation of selected two-dimensional techniques for computed tomography quantitation of lymph nodes," Investigative Radiology, vol. 31, no. 3, pp. 138-145, 1996.

96. A. Rosenfeld, A. C. Kak, Digital Image Processing. New York: Academic Press, 1982.

97. J. C. Russ, The Image Processing Handbook. Boca Raton: CRC Press, 1999.

98. P. K. Sahoo, S. Soltani, A. K. Wond, Y. C. Chen, "A survey of thresholding techniques," Comput. Vision, Graph, Image Process., vol. 41, pp. 233-260, 1988.

99. P. Schmid, "Segmentation of digitized dermatoscopic images by two-dimensional color clustering," IEEE Trans. on Med. Imag, vol. 18, no. 2, pp. 164-171, 1999.

100. T. Schiemann, J. Nuthmann, U. Tiede, and K. H. Höhne, "Segmentation of the Visible Human for high quality volume based visualization", In: Visualization in Biomedical Computing, Proc. VBC '96 (K. H. Höhne and R. Kikinis, eds.), vol. 1131 of Lecture Notes in Computer Science, pp. 13-22, Berlin: Springer-Verlag, 1996.

101. N. Shareef, D. L. Wand, R. Yagel, "Segmentation of medical images using LEGION,'' IEEE Trans. Med. Imag., vol. 18, no. 1, pp. 74-91, 1999.

102. L. Shen, R. Rangayyan, and J. E. L. Desautels, "Detection and classification of mammographic calcifications,'' International Journal of Pattern Recognition and Artificial Intelligence, vol. 7, pp. 1403-1416, 1993.

103. A. Simmons, S. R. Arridge, G. J. Barker, A. J. Cluckie, P. S. Tofts, "Improvements to the quality of MRI cluster analysis,'' Mag. Res. Imag., vol. 12, no. 8, pp. 1191-1204, 1994.

104. H. R. Singleton, G. M. Pohost, "Automatic cardiac MR image segmentation using edge detection by tissue classification in pixel neighborhoods,'' Mag. Res. Med., vol. 37, pp. 418-424, 1997.

105. M. Sonka, X. Zhang, M. Siebes, M. S. Bissing, S. C. DeJong, S. M. Collins, C. R. McKay, "Segmentation of intravascular ultrasound images: a knowledge-based approach,'' IEEE Trans. Med. Imag., vol. 14, no. 4, pp. 719-731, 1955.

106. M. Sonka G. K. Reddy, M. D. Winniford, S. M. Collins, "Adaptive approach to accurate analysis of small-diameter vessels in cineangiograms,'' IEEE Trans Med. Imag., vol. 16, no. 1, pp. 87-95, 1997.

107. M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis, and Machine Vision. CA: PWS Publishing, Pacific Grove, 1999.

108. D. Spielman, M. Sidhu, R. Herfkens, L. Shortlife, "Correlation imaging of the kidney,'' Proceedings of the

International SMRM Conference, Nice, France, p. 373, 1995.

109. P. Suetens, E. Bellon, D. Vandermeulen, M. Smet, G. Marchal, J. Nuyts, L. Mortelman, "Image segmentation: methods and applications in diagnostic radiology and nuclear medicine," European Journal of Radiology, vol. 17, pp. 14-21, 1993.

110. Y. Sun, R. J. Lucariello, S. A. Chiaramida, "Directional low-pass filtering for improved accuracy and reproduci-bility of stenosis quantification in coronary arteriograms," IEEE Trans. Med. Imag., vol. 14, no. 2, pp. 242-248, 1995.

111. T. Taxt, A. Lundervold, B. Fuglaas, H. Lien, V. Abeler, "Multispectral analysis of uterine corpus tumors in magnetic resonance imaging," Magn. Res. Med., vol. 23, pp. 55-76, 1992.

112. D. R. Thedens, D. J. Skorton, S. R. Feagle, "Methods of graph searching for border detection in image sequences with applications to cardiac magnetic resonance imaging," IEEE Trans. Med. Imag, Vol. 14, no. 1, pp. 42-55, 1995.

113. V. Torre, T. A. Poggio, "On edge detection," IEEE Trans PAMI, vol 8, pp. 147-163, 1986.

114. D. L. Toulson, J. F. Boyce, "Segmentation of MR images using neural nets," Image Vision, Computing, vol. 5, pp. 324-328, 1992.

115. J. K. Udupa, L. Wei, S. Samarasekera, Y. Miki, M. A. van Buchem, R. I. Grossman, "Multiple sclerosis lesion quantification using fuzzy-connectedness principles," IEEE Trans. Med. Imag., vol. 16, no. 5, 1997.

116. S. E. Umbaugh, Computer Vision and Image Processing: A Practical Approach Using CVIPtools. Upper Saddle River, NJ: Prentice Hall, 1998.

117. M. W. Vannier, R. L. Butterfield, D. Jordan, W. A. Murphy, R. G. Levitt, M. Gado, "Multispectral analysis of magnetic resonance images," Radiology, vol. 154, pp. 221-224, 1985.

118. M. W. Vannier, C. M. Speidel, D. L. Rickman, "Magnetic resonance imaging multispectral tissue classification," News Physiol. Sci., vol. 3, pp. 148-154, 1988.

119. L. Vincen, P. Soille, "Watersheds in digital spaces: an efficient algorithm based on immersion simulations," IEEE Trans. PAMI, vol. 13(6), pp. 583-598, 1991.

120. L. Vincent, "Morphological grayscale reconstruction in image analysis: applications and efficient algorithms," IEEE Trans. Image Processing, vol. 2(2), pp. 176-201,1993.

121. J. Z. Wang, D. A. Turner, M. D. Chutuape, "Fast, interactive algorithm for segmentation of series of related images: application to volumetric analysis of MR images of the heart," JMRI, vol. 2, no. 5, pp. 575-582, 1992.

122. S. K. Warfield, M. Kaus, F. A. Jolesz, R. Kikinis, "Adaptive template moderated spatially varying statistical classification," Proc. of First Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'98), pp. 431-438, in Lecture Notes in Computer Science, Eds.

W.M. Wells, A. Colchester, and S. Delp, Springer Verlag, 1998.

123. S. K. Warfield, M. K. Ferenc, A. Jolesz, R. Kikinis, "Adaptive template moderated spatially varying statistical classification," Medical Image Analysis, 2000 (accepted to appear).

124. J. B. Weaver, Y. Xu, D. M. Healy, L. D. Cormwell, "Filtering noise from images with wavelet transforms," Magn. Res. Med., vol. 21, pp. 288-295, 1991.

125. W. M. Wells III, E. L. Grimson, R. Kikinis, F.A. Jolesz, "Adaptive segmentation of MRI data," IEEE Trans. Med. Imag., vol. 15, no. 4, pp. 429-443, 1996.

126. C-F. Westin, S. Warfield, A. Bhalerao, L. Mui, J. Richolt, R. Kikinis, "Tensor controlled local structure enhancement of CT images for bone segmentation," Proc. of First Int. Conf. on Medical Image Computing and ComputerAssisted Intervention (MICCAI'98), pp. 1205-1212, in Lecture Notes in Computer Science, Eds. W.M. Wells, A. Colchester, and S. Delp, Springer Verlag, 1998.

127. J. S. Weszka. "A survey of threshold selection techniques," Computer Graphics and Image Proc., vol. 7, pp. 259-265, 1978.

128. A. Witkin, M. Kass, D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, vol. 1(4), pp. 321-331, 1988.

129. A. Yezzi, S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, "A geometric snake model for segmentation of medical imagery," IEEE Trans. Med. Imag., vol. 16, no. 2, pp. 199-209, 1997.

130. H. Yoshida, K. Doi, R. M. Nishikawa, "Automated detection of clustered microcalcifications in digital mammograms using wavelet transform techniques," SPIE Image Processing, vol. 2167, pp. 868-886, 1994.

131. X. Zeng, L. H. Staib, R. T. Schultz, J. S. Duncan, "Volumetric layer segmentation using coupled surfaces propagation," Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 708-715, Santa Barbara, CA, June 1998.

132. Y. Zhu, H. Yan, "Computerized tumor boundary detection using a Hopfield neural network," IEEE Trans. Med. Imag., vol. 16, no. 1, pp. 55-67, 1997.

133. S. W. Zucker, "Region growing: childhood and adolescence," Comp. Graphics Image. Proc., vol. 5, pp. 382-399, 1976.

Essentials of Human Physiology

Essentials of Human Physiology

This ebook provides an introductory explanation of the workings of the human body, with an effort to draw connections between the body systems and explain their interdependencies. A framework for the book is homeostasis and how the body maintains balance within each system. This is intended as a first introduction to physiology for a college-level course.

Get My Free Ebook

Post a comment