Abstract
We present two applications of model based computer vision methods to measurement of image features significant in the diagnosis of diabetic neuropathy. The first involves the location of the boundaries of nerve fascicles in light microscope images. The second involves the segmentation of capillary cell regions using electron microscope images. In each case the boundaries required are of arbitrary shape and characterised by local texture or changes in textured regions.
The fascicular boundary is located using an Active Contour Model responding to a texture measure based on edge directionality. A start position for the model is automatically generated. The capillary segmentation is performed using a region-based snake responding to a weighted combination of texture measures followed by a local boundary refinement using dynamic programming. These methods show that application of various types of Active Contour Model, accompanied by appropriate starting cues, or followed by local refinements, can locate robustly positioned and intuitively correct boundaries in these images. The aim of the work is the automation of diagnostic measurements currently performed manually. We discuss the implications of automated analysis for procedures in quantitative histology.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
A.A. Amir, Using Dynamic Programming for Solving Variational Problems in Vision, IEEE Trans. PAMI, 12(9), 1990, pp.855–867.
S.T. Britland, R.J. Young, A.K. Sharma, B.F. Clarke, Acute and Remitting Painful Diabetic Neuropathy: A Comparison of Peripheral Nerve Fibre Neuropathy, Pain, 48, 1992, pp.316–370.
J. Canny, A Computational Approach to Edge Detection, IEEE Trans. PAMI, 8(6), 1986, pp679–698
L.D. Cohen, On Active Contours and Balloons, CVGIP, Image Understanding, 53(2), 1991, pp21–218
T.F. Cootes, C.J. Taylor, D.H. Cooper, J. Graham, Active Shape Models: Their Training and Application. Computer Vision and Image Understanding 61, 1995, pp.38–59.
T.F. Cootes, A. Hill, C.J. Taylor, J. Haslam. Use of Active Shape Models for Locating Structure in Medical Images, Proc. IPMI (13), 1993, pp33–47.
S.R. Gunn, M.S. Nixon, A Model-Based Dual Active Contour, Proc. BMVC94, BMVC Press 1994, pp305–314.
RM. Haralick, L.G. Shapiro. Computer and Robot Vision Vol.1 Addison-Wesley 1992 pp221–223.
J. Ivins, J. Porrill, Statistical Snakes: Active Region Models, Proc. BMVC 94, BMVC Press 1994, pp377–386.
M. Kass, A. Witkin, D. Terzopoulos. Snakes: Active Contour Models. Proc. 1st Intl. Conf. Computer Vision. 1987, pp259–266.
K.I. Lavs. Rapid Texture Identification. SPIE Conf. on Image Processing for Missile Guidance. vol.238 1980, pp376–380.
J.P. Lutkin. Interactive Segmentation of Medical Images. Msc Thesis Manchester University, 1994.
R.A. Malik, S. Tesfaye, S.D. Thompson, A. Veves, A.K. Sharma, A.J.M. Boulton, J.D. Ward. Microangiopathy in Human Diabetic Neuropathy: Relationship Between Capillary Abnormalities and the Severity of Neuropathy. Diabetologia, 30, 1989, pp.92–102.
R.A. Malik, P.G. Newrick, A.K. Sharma, A. Jennings, A.K. Ah-See, A.J.M. Boulton, J.D. Ward. Endoneurial Localisation of Micro vascular Damage in Human Diabetic Neuropathy, Diabetologia 36, 1993 pp 454–459.
R. Sutton, E. Hall. Texture Measures for Automatic Segmentation of Pulmonary Diseases, IEEE Trans. Comp. c-21. 1972, pp667–678.
R.G. Tilton, P.L. Hoffman, C. Kilo, J.R. Williamson. Pericyte Degeneration and Basement Membrane Thickening in Skeletal Capillaries of Human Diabetes, Diabetes 30, 1981, pp.326–334.
D.J. Williams, M. Shah. A Fast Algorithm for Active Contours and Curvature Estimation. CVGIP: Image Undrstanding vol.55(1) 1992, pp14–26.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Byrne, M.J., Graham, J. (1996). Application of model based image interpretation methods to diabetic neuropathy. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61123-1_146
Download citation
DOI: https://doi.org/10.1007/3-540-61123-1_146
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61123-3
Online ISBN: 978-3-540-49950-3
eBook Packages: Springer Book Archive