Statistical analysis of optical coherent tomography image by segmentation methods using euclidean distant model
-
2018-05-03 https://doi.org/10.14419/ijet.v7i2.25.16572
-
Segmentation, Multidimensional Scaling (MDS), Euclidean Distance Model, Statistical Image Analysis. -
Abstract
Optical Coherence Tomography (OCT) is a diagnostic measure of the images in retinal layers. The measure of retinal layers is done by image segmentation algorithm. In this work, algorithm of different threshold based segmentation techniques is computed in OCT image. Using the resultant image, comparison is done between the segmentation methods by statistical parameters such as noise factor of the signal peak, Normalized Absolute Error (NAE) and Average Difference (AD). Statistical analysis values are evaluated by multidimensional scaling (MDS) methods. By applying clustering methods in the multidimensional data, the Euclidean Distance model gives the relation distant between the algorithms. By this result, similarity or dissimilarity of segmentation methods was categorized.
Â
Â
-
References
[1] J. Natalia, C. Anastasova, "A Review of Multidimensional Scaling(MDS) and its Utility in Various Psychological Domains", Tutorials in Quantitative Methods for Psychology, University of Ottawa, Vol. 5(1), p. 1-10, 2009.
[2] Sankur B., Sezginb M. Image Thresholding Techniques: a Survey over Categories. Journal of Electronic Imaging, vol. 13(1), (2004) pp. 146-165.
[3] Mehmet Sezgin, BulentSankur, "Survey over image thresholding techniques and quantitative performance evaluation", Journal of Electronic Imaging, 13(1), 146–165 Jan. 2004.
[4] T.W. Ridler, S. Calvard, Picture thresholding using an iterative selection method, IEEE Trans. System, Man and Cybernetics, SMC-8 (1978) 630-632.
[5] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
[6] D.E. Lloyd, Automatic Target Classification Using Moment Invariant of Image Shapes, Technical Report, RAE IDN AW126, Farnborough- UK, December 1985.
[7] J. Kittler & J. Illingworth: "Minimum Error Thresholding†Pattern Recognition, Vol 19, nr 1. 1986, pp. 41-47.
[8] M.K. Yanni, E. Horne, and A New Approach to Dynamic Thresholding, EUSIPCO-9: European Conf. on Signal Processing, Vol. 1, Edinburg, 1994, pp: 34-44.
[9] J.N. Kapur, P.K. Sahoo, A.K.C. Wong, A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram,†Graphical Models and Image Processing, 29 (1985) 273-285.
[10] W.H. Tsai, Moment-preserving thresholding: A new approach, Graphical Models and Image Processing, 19 (1985) 377-393.
[11] W. Niblack, an Introduction to Image Processing, Prentice-Hall, 1986, pp: 115-116.
[12] P.W. Palumbo, P. Swaminathan, S.N. Srihari, Document image binarization: Evaluation of algorithms, Proc. SPIE Applications of Digital Image Proc., SPIE Vol. 697, (1986), pp:278-286.
[13] J. Sauvola, M. Pietaksinen, Adaptive document image binarization, Pattern Recognition, 33 (2000) 225-236. C.K. Leung, F.K. Lam, Performance analysis of a class of iterative image thresholding algorithms, Pattern Recognition, 29(9) 1523-1530, 1996.
[14] G Mohandass, Dr. R. Anandanatarajan "Statistical Analysis of Reliability in edge detection techniques using Optical Coherent Tomography image" Indian Journal of Computer Science and Engineering (IJCSE), ISSN: 0976-5166, Vol. 4 No.2 Apr-May 2013.
-
Downloads
-
How to Cite
J. Hemalatha, R., G, M., & Krishnan G, H. (2018). Statistical analysis of optical coherent tomography image by segmentation methods using euclidean distant model. International Journal of Engineering & Technology, 7(2.25), 121-124. https://doi.org/10.14419/ijet.v7i2.25.16572Received date: 2018-07-30
Accepted date: 2018-07-30
Published date: 2018-05-03