Development and Experimental Investigation of Mathematical Methods for Automating the Diagnostics and Analysis of Ophthalmological Images
Tóm tắt
The paper summarizes the joint work of specialists in the fields of image analysis and ophthalmology over the last few years. As a result of this work, new mathematical methods for automating image analysis that have important diagnostic value for ophthalmology have been developed: (1) identification of the lipid layer state in the intermarginal space of human eyelids; (2) analysis of the degree of cellular structure density (cellularity) in the corneal tissue of human eyes; (3) identification of the state of the retinal blood flow when analyzing fluorescent angiograms of the human fundus; and (4) morphometric analysis of the state of the epithelium posterius (endothelium) in the human eye cornea. As initial data, we used (respectively) (1) images of imprints of the eyelid intermarginal space on a millipore filter upon their osmium vapor staining; (2) micrographs of corneal tissue specimens obtained using a light microscope; (3) fluorescent angiograms of the human fundus; and (4) images of endothelial cells obtained noninvasively using a confocal microscope. The developed methods are designed to extract morphometric data from these images. For each problem, the following results were obtained: (1) expectations and variances of pixel intensities on the imprint along a drawn line and over a selected region, as well as plots that characterize pixel intensity and change in the thickness of the imprint along a drawn line; (2) expectations and variances for the intensities of the selected regions and intensity histograms; (3) extracted vessels and ischemia zones with their statistical descriptions; and (4) detected cells of hexagonal, pentagonal, and other shapes, as well as a set of characteristics associated with the size of the cells detected. The developed methods are based on the fundamental results of the mathematical theory of image analysis and on the joint use of image processing, mathematical morphology, and mathematical statistics techniques. The paper also describes software implementations of the developed methods, including an automated research workstation for ophthalmologists, and presents the results of their experimental testing.
Tài liệu tham khảo
S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two–dimensional matched filters,” IEEE Trans. Med. Imaging 8 (3), 263–269 (1989).
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. (Prentice–Hall, Upper Saddle River, NJ, 2002).
I. B. Gurevich, D. V. Harazishvili, O. Salvetti, A. A. Trykova, and I. A. Vorob’ev, “Elements of the information technology of cytological specimens analysis: Taxonomy and factor analysis,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 16 (1), 113–115 (2016).
I. B. Gurevich, V. V. Yashina, A. A. Fedorov, A. M. Nedzved’, and A. M. Ospanov, “Development, investigation, and software implementation of a new mathematical method for automated identification of the lipid layer state by images of eyelid intermarginal space,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 27 (3), 538–549 (2017).
I. B. Gurevich, V. V. Yashina, A. A. Fedorov, A. M. Nedzved’, and A. T. Tleubaev, “Development, investigation, and software implementation of a new mathematical method for automating the analysis of corneal endothelium images,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 27 (3), 550–559 (2017).
I. B. Gurevich, A. A. Myagkov, Yu. O. Trusova, V. V. Yashina, and Yu. I. Zhuravlev, “On basic problems of image recognition in neurosciences and heuris–tic methods for their solution,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 25 (1), 132–160 (2015).
R. M. Haralick and L. G. Shapiro, Computer and Robot Vision, Vol. 1 (Addison–Wesley, Reading, MA, 1992). Chap. 5.
P. F. M. Nacken, “Chamfer metrics, the medial axis and mathematical morphology,” J. Math. Imaging Vis. 6 (2–3), 235–248 (1996).
A. Nedzved, O. Nedzved, A. Glinsky, G. Karapetian, I. Gurevich, and V. Yashina, “Detection of dynamical properties of flow in an eye vessels by video sequences analysis,” in Proc. Int. Conf. on Information and Digital Technologies (IDT) 2017 (Žilina, Slovakia, July 5–7, 2017) (IEEE, 2017), pp. 275–280.
M. Petrou and C. Petrou, Image Processing: The Fundamentals, 2nd ed. (Wiley, Chichester, 2010).
T. N. Safonova, A. A. Fedorov, A. O. Zabegaylo, G. B. Egorova, and T. S. Mitichkina, “Treatment of dry eye syndrome by primary glaucoma,” Russian J. Glaucoma 14 (4), 36–43 (2015).
J. Sauvola and M. Pietikainen, “Adaptive document image binarization,” Pattern Recogn. 33 (2), 225–236 (2000).
L. G. Shapiro and G. C. Stockman, Computer Vision (Prentice–Hall, 2001). pp. 137, 150.
J. V. B. Soares, J. J. G. Leandro, R. M. Cesar–Jr., H. F. Jelinek, and M. J. Cree, “Retinal vessel segmentation using the 2–D Morlet wavelet and supervised classification,” IEEE Trans. Med. Imaging 25 (9), 1214–1222 (2006).
P. Soille, Morphological Image Analysis: Principles and Applications, 2nd ed. (Springer–Verlag, Berlin, Heidelberg, 2003 and 2004).
J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge–based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging 23 (4), 501–509 (2004).
K. A. Vermeer, F. M. Vos, H. G. Lemij, and A. M. Vossepoel, “A model based method for retinal blood vessel detection,” Comput. Biol. Med. 34 (3), 209–219 (2004).
M. Vlachos, E. Dermatas, “Multi–scale retinal vessel segmentation using line tracking,” Comput. Med. Imaging Graphics 34 (3), 213–227 (2010).
G. O. Waring, W. M. Bourne, H. F. Edelhauser, and K. R. Kenyon, “The corneal endothelium. Normal and pathologic structure and function,” Ophthalmology 89 (6), 531–699 (1982).
B. Zhang, L. Zhang, L. Zhang, and F. Karray, “Retinal vessel extraction by matched filter with first–order derivative of Gaussian,” Comput. Biol. Med. 40 (4), 438–445 (2010).
L. Zhou, M. S. Rzeszotarski, L. J. Singerman, and J. M. Chokreff, “The detection and quantification of retinopathy using digital angiograms,” IEEE Trans. Med. Imaging 13 (4), 619–626 (1994).