AORTA software system for evaluating individual predisposition to atherosclerosis on the basis of genetic and phenotypic markers

• Autorzy: Karpenko A. , Kharlamov V. , Sobenin I.

Identyfikatory

DOI

10.1515/bams-2014-0023

• Języki publikacji: EN

Abstrakty

EN

This article deals with the AORTA software system providing support for research activities to find molecular basis for further assessment of individual predisposition to atherosclerosis. These studies are aimed at finding a relationship between somatic mutations of the mitochondrial genome in the aortic wall cells and the extent of atherosclerotic lesions of the aorta. A morphologist selects these areas on an aortic tissue sample and describes them, so that within each area, deviation of the quantitative indicator of atherosclerosis severity (phenotypic marker) from the area average should be sufficiently small. Next, the frequency and severity indicators of somatic mutations of the mitochondrial genome (genetic markers) are measured for each area and then entered into the AORTA system.

Słowa kluczowe

EN

aortic wall; atherosclerosis; genetic and phenotypic markers; image processing; somatic mutations

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; De Gruyter
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2015
• Tom: Vol. 11, no. 1
• Strony: 25--32
• Opis fizyczny: Bibliogr. 21 poz., rys., wykr.

Twórcy

autor

Karpenko A.

• Computer-Aided Design, Bauman Moscow State Technical University, 2-ya Baumanskaya ul. 5, Moscow 105005, Russian Federation

autor

Kharlamov V.

• MSTU n.a. Bauman, Moscow, Russian Federation

autor

Sobenin I.

• Russian Cardiology Research and Production Complex, 3-ya Cherepkovskaya 15-a, 121552, Moscow, Russian Federation
• Institute of General Pathology and Patophysiology, Baltiyskaya ul., 5, 125315, Moscow, Russian Federation

Bibliografia

• 1. Sazonova M, Budnikov E, Khasanova Z, Sobenin I, Postnov A, Orekhov A. Studies of the human aortic intima by a direct quantitative assay of mutant alleles in the mitochondrial genome. Atherosclerosis 2009;204:184–90.
• 2. Pratt W. Digital image processing. New York: John Wiley & Sons, 1978:750.
• 3. Shapiro LG, Stockman GC. Computer vision. Upper Saddle River, NJ: Prentice-Hall, 2001:444.
• 4. Marr DC. Vision: a computational investigation into the human representation and processing of visual information. New York: W.H. Freeman & Company, 1982:397.
• 5. Gonzalez RC, Woods RE. Digital image processing, 2th ed. Upper Saddle River, NJ: Prentice-Hall, 2008:779.
• 6. Vezhnevets A, Barinova O. Image segmentation methods: automatic segmentation. computer graphics and multimedia. Network J 2006;4. Available at: http://cgm.computergraphics.ru/content/view/147. Accessed: 1 Feb 2015.
• 7. Shi J, Malik J. Normalized cuts and image segmentation. Proc IEEE Conf Comput Vis Pattern Recog 1997:731–7.
• 8. Sharon E, Brandt A, Basri R. Fast multiscale image segmentation. Proc IEEE Conf Comput Vis Pattern Recog 2000;1:70–7.
• 9. Koker R, Sari Y. Neural network based automatic threshold selection for an industrial vision system. Proc Int Conf Signal Process 2003:523–5.
• 10. Papamarkos NA. Technique for fuzzy document binarization. Proc ACM Symp Document Eng 2001:152–6.
• 11. Wu K, Otoo E, Suzuki K. Optimizing two-pass connected-component labeling algorithms. Pattern Anal Appl 2009;12: 206–20.
• 12. Haralick RM. Some neighborhood operations. In: Onoe M, Preston K Jr, Rosenfeld A, editors. Real time-parallel computing: image analysis. New York: Plenum Press, 1981:11–35.
• 13. Suzuki K, Horiba I, Sugie N. Linear-time connected-component labeling based on sequential local operations. Comput Vis Image Underst 2003:89:1–23.
• 14. Rosenfeld A, Pfaltz P. Sequential operations in digital picture processing. J Assoc Comput Mach 1966;12:471–94.
• 15. Chang F, Chen C-J, Lu C-J. A linear time component-labeling algorithm using contour tracing technique. Comput Vis Image Underst 2004;93:206–20.
• 16. Shapiro L. Connected component labeling and adjacency graph construction. In: Topological algorithms for digital image processing. Amsterdam: Elsevier, 1996:1–31.
• 17. He L, Chao Y, Suzuki K, Wu K. Fast connected-component labeling. Pattern Recog 2009;42:1977–87.
• 18. Sterzhanov MB. Methodology of selecting connected components in line binary images. Minsk: Belarusian State University of Informatics and Radioelectronics (BSUIR), 2006:18.
• 19. Ohlander R, Price K, Reddy DR. Picture segmentation using a recursive region splitting method. Comput Graphics Image Process 1978;8:313–33.
• 20. Karypis G, Kumar V. Multilevel k-way partitioning scheme for irregular graphs. J Parallel Distrib Comput 1998;8: 96–129.
• 21. Sazonova MA, Sinyov VV, Barinova VA, Ryzhkova AI, Zhelankin AV, et al. Mosaicism of mitochondrial genetic variation in atherosclerotic lesions of the human aorta. BioMed Res Int 2014: article ID 825468.