Диссертация (Автоматизированная бактериоскопическая диагностика туберкулеза), страница 37
Описание файла
Файл "Диссертация" внутри архива находится в папке "Автоматизированная бактериоскопическая диагностика туберкулеза". PDF-файл из архива "Автоматизированная бактериоскопическая диагностика туберкулеза", который расположен в категории "". Всё это находится в предмете "медицина" из Аспирантура и докторантура, которые можно найти в файловом архиве РНИМУ им. Пирогова. Не смотря на прямую связь этого архива с РНИМУ им. Пирогова, его также можно найти и в других разделах. , а ещё этот архив представляет собой докторскую диссертацию, поэтому ещё представлен в разделе всех диссертаций на соискание учёной степени доктора медицинских наук.
Просмотр PDF-файла онлайн
Текст 37 страницы из PDF
–Langebaan, 2005. – P. 183–189.322. Russell, M. Autofocusing and Image Segmentation in Microscopy forAutomatic Detection of Tuberculosis in Sputum Smears / M. Russel // MSc (Med) Thesis,Department of Human Biology, University of Cape Town. – 2006. – 102 p.323. Segmentation Approach Towards Phase-Contrast Microscopic Images ofActivated Sludge to Monitor the Wastewater Treatment / M. B. Khan, H.
Nisar, C. A. Ng[et al.] // Microsc. Microanal. – 2017. – Vol. 23, № 6. – P. 1130–1142.324. Segmentation of White Blood Cells From Microscopic Images Using aNovel Combination of K-Means Clustering and Modified Watershed Algorithm /N. Ghane, A. Vard, A. Talebi [et al.] // J. Med. Signals Sens. – 2017. – Vol. 7, № 2.
–P. 92–101.250325. Segmentation, autofocusing and signature extraction of tuberculosis sputumimages / M. Forero-Vargas, F. Sroubek, J. Alvarez-Borrego [et al.] // In Proc. of SPIEPhotonic Devices and Algorithms for Computing IV. – 2002. – P. 171–182.326. Seidenari, S. Digital videomicroscopy and image analysis with automaticclassification for detection of thin melanomas / S. Seidenari, G. Pellacani, A. Giannetti //Melanoma.
Res. – 1999. – Vol. 9, № 2. – P. 163–171.327. Serial image analysis of Mycobacterium tuberculosis colony growth revealsa persistent subpopulation in sputum during treatment of pulmonary TB / D. Barr,M. Kamdolozi, Y. Nishihara [et al.] // Tuberculosis. – 2016. – Vol. 98. – P. 110–115.328. Shaker, F. Automatic detection and segmentation of sperm head, acrosomeand nucleus in microscopic images of human semen smears / F. Shaker,S. A.
Monadjemi, A. R. Naghsh-Nilchi // Comput. Methods Programs Biomed. – 2016. –№ 132. – P. 11–20.329. Shape symmetry analysis of breast tumors on ultrasound images / W. Yang,S. Zhang, Y. Chen [et al.] // Comput. Biol. Med. – 2009. – Vol. 39, № 3. – P. 231–238.330. Skin image retrieval using Gabor wavelet texture feature / X.
Ou, W. Pan,X. Zhang [et al.] // International journal of cosmetic science. – 2016. – № 6. – P 607–614.331. Smithson, R. C. Quantitative simulation of image correction for astronomywith a segmented active mirror / R. C. Smiyhson, M. L. Peri, R. S. Benson // Appl. Opt.– 1988.
– Vol. 27, № 8. – P. 1615-1620.332. Sotaquirá, M. Detection and quantification of bacilli and clusters present insputum smear samples: a novel algorithm for pulmonary tuberculosis diagnosis /M. Sotaquirá, L. Rueda, R. Narvaez // In Proc.of International Conference on DigitalImage Processing. – Bangkok, 2009. – P. 117–121.333. Staib, L. H. Model-based deformable surface finding for medical images /L. H. Staib, J. S. Duncan // IEEE Trans. Med. Imaging.
– 1996. – Vol. 15, № 5. – P. 720–731.334. Stokman, H. Selection and fusion of color models for image featuredetection / H. Stokman, T. Gevers // IEEE Trans. Pattern Anal. Mach. Intell. – 2007. –Vol. 29, № 3. – P. 371–381.251335. Sturges, H. The choice of a class-interval / H. Sturges // Journal of theAmerican Statistical Association. – 1926.
– № 21. – P. 65–66.336. Su, B. Robust document image binarization technique for degradeddocument images / B. Su, S. Lu, C. L. Tan // IEEE Trans Image Process. – 2013. –Vol. 22, № 4. – P. 1408–1417.337. Sujana, H. Application of artificial neural networks for the classification ofliver lesions by image texture parameters / H. Sujana, S. Swarnamani, S. Suresh //Ultrasound Med. Biol. – 1996.
– Vol. 22, № 9. – P. 1177–1181.338. Sund, T. An algorithm for fast adaptive image binarization with applicationsin radiotherapy imaging / T. Sund, K. Eilertsen // IEEE Trans. Med. Imaging. – 2003. –Vol. 22, № 1. – P. 22–28.339. Supervised nonlinear dimension reduction of functional magnetic resonanceimaging data using Sliced Inverse Regression / Y. Tu, A.
Tan, Z. Fu [et al.] // Conferenceproceedings : 37th Annual International Conference of the IEEE Engineering in Medicineand Biology Society. – Milan, 2015. – P. 2641–2645.340. Support Vector Machines for Automatic detection of Tuberculosis Bacteriain Confocal Microscopy Images / B. Lenseigne, P. Brodin, T. Christophe [et al.] // Proc.of the 4th IEEE Symposium on Biomedical Imaging: From Nano to Macro.
– Arlington,2007. – P. 85–87.341. Survey of physician use of radiography and sputum smear microscopy fortuberculosis diagnosis and follow-up in Botswana / R. E. Huebner, T. L. Moeti,N. J. Binkin [et al.] // Int. J. Tuberc. Lung Dis. – 1997. – Vol. 1, № 4. – P. 333–338.342.
Tadrous, P. J. Computer-Assisted Screening of Ziehl-Neelsen–StainedTissue for Mycobacteria / P. J. Tadrous // Am. J. Clin. Pathol. – 2010. –№ 133. – P. 849–858.343. Tektonidis, M. Non-rigid multi-frame registration of cell nuclei in live cellfluorescence microscopy image data / M. Tektonidis // Medical Image Analysis. – 2015.– №19. – P. 1–14.252344. Texture and color based image segmentation and pathology detection incapsule endoscopy videos / P.
Szczypiński, A. Klepaczko, M. Pazurek [et al.] // Comput.Methods Programs Biomed. – 2014. – Vol. 113, № 1. – P. 396–411.345. The Automated Identification of Tubercle Bacilli using Image Processingand Neural Computing Techniques / K. Veropoulos, C. Campbell, G. Learmonth // InProc. of the 8th International Conference on Artificial Neural Networks. – Skövde, 1998.– P. 797–802.346. The contribution of image cytometry and artificial intelligence-relatedmethods of numerical data analysis for adipose tumor histopathologic classification /D. Goldschmidt, C. Decaestecker, J. V. Berthe // Lab. Invest.
– 1996. – Vol. 75, № 3. –P. 295–306.347. The role of chest radiography in the suspicion for and diagnosis ofpulmonary tuberculosis in intensive care units / J.-Y. Wu, S.-C. Ku, C.-C. Shu [et al.] //Int. J. Tuberc. Lung Dis. – 2009. – Vol. 13, № 11. – P. 1380–1386.348. Three-dimensional image correction of tilted samples through coordinatetransformation / J. Fu, W. Chu, R. Dixson [et al.] // Scanning. – 2008. – Vol.
30, № 1. –P. 41–46.349. Time continuous tracking and segmentation of cardiovascular magneticresonance images using multidimensional dynamic programming / M. Uzümcü, R. J. vander Geest, C. Swingen [et al.] // Invest. Radiol. – 2006. – Vol. 41, № 1. – P. 52–62.350. Time sequence image analysis of positron emission tomography usingwavelet transformation / Hsu C.Y., Lai Y.L., Chen C.C., Lee Y.T., Tseng K.K., Lai Y.K.,Zheng C.Y., Jheng H.C. // Technology and health care. – 2015. – №24. – P.393-400.351. Tsuruta, T. Image correction using holography / T. Tsuruta, Y.
Itoh // Appl.Opt. – 1968. – Vol. 7, № 10. – P. 2139–2140.352. Tuberculosis among foreign-born persons in the united states: achievingtuberculosis elimination / K. P. Cain, C. A. Haley, L. R. Armstrong [et al.] // Am. J.Respir. Crit. Care Med. – 2007. – Vol. 175, № 1. – P. 75–79.253353.
Tuberculosis epidemiology in Russia: themathematical model and dataanalysis / M. Perelman, G. I. Marchuk, S. E. Borisov [et al.] // Russ. J. Numer. Anal.Math. Modelling. – 2004. – Vol. 19, № 4. – P. 305–314.354. Uchiyama, T. Color Image Segmentation using Competitive Learning /T. Uchiyama, M. A. Arbib / IEEE Transactions on Pattern Analysis and MachineIntelligence. – 1994.
– Vol. 16, № 12. – P. 1197–1206.355. Unsupervised CT Lung Image Segmentation of a MycobacteriumTuberculosis Infection Model / P. M. Gordaliza, A. Muñoz-Barrutia, M. Abella // Sci.Rep. – 2018. – Vol. 8, № 1. – P. 9802.356. Using Fisher information to track stability in multivariate systems /N. Ahmad, S. Derrible, T.
Eason [et al.] // R. Soc. Open Sci. – 2016. – Vol. 3, № 11. –e160582.357. Validation of the interleaved pyramid for the segmentation of 3D vectorimages, C.N.Graaf, A.S.E.Koster, K.L.Vincken and M.A.Viergever. Pattern RecognitionLetters, 15, 1994, pp.467-475.358. Varga, V. S. Scanning Fluorescent Microscopy is an Alternative forQuantitative Fluorescent Cell Analysis / V. S.
Varga // Cytometry. – 2004. – С. 53–62.359. Veropoulos, K. Image processing and neural computing used in thediagnosis of tuberculosis / K. Veropoulos, C. Campbell, G. Learmonth // In Proc. of IEEColloquium on Intelligent Methods in Healthcare and Medical Applications – York,1998. – N. 8/1–8/4.360. Walter, R. J. Computer-Enhanced Video Microscopy: Digitally ProcessedMicroscope Images Can Be Produced in Real Time / R. J. Walter // Cell Biology.
– 1981.– Vol. 78, № 11. – P. 6927–6931.361. Wang, X. Laplacian operator-based edge detectors / X. Wang // IEEE Trans.Pattern. Anal. Mach. Intell. – 2007. – Vol. 29, № 5. – P. 886–890.362. Watanabe, K. Semi-Supervised Feature Transformation for Tissue ImageClassification / K. Watanabe, T. Kobayashi, T. Wada // PLoS One. – 2016. – Vol. 11,№ 12.
– e0166413.254363. Wavelet analysis of cardiac optical mapping data / F. Xionga, X. Oib,S. Nattela [et al.] // Computers in Biology and Medicine. – 2015. – №65. – P. 243–255.364. Weakly-supervised structured output learning with flexible and latent graphsusing highorder loss functions / G. Carneiro, T. Peng, C. Bayer [et al.] // In Proc. IEEEInt. Conf. Comput. Vis.
Dec. – 2015. – P. 648–656.365. Wicker, K. Phase optimisation for structured illumination microscopy /K. Wicker // Optics Express. – 2013. – Vol. 78, № 2. – P. 2032–2049.366. Wong, K. C. L. Building medical image classifiers with very limited datausing segmentation networks / K.
C. L. Wang, T. Syeda-Mahmood, M. Moradi // Med.Image Anal. – 2018. – № 49. – P. 105–116.367. Wouters, K. A non-invasive fluorescent staining procedure allows ConfocalLaser Scanning Microscopy based imaging of Mycobacterium in multispecies biofilmscolonizing and degrading polycyclic aromatic hydrocarbons / K. Wouters // Journal ofMicrobiological Methods. – 2010. – № 83. – С. 317–325.368. Xu, Y. Image correction algorithm for functional three-dimensional diffuseoptical tomography brain imaging / Y. Xu, H. L. Graber, R. L. Barbour // Appl. Opt. –2007. – Vol. 46, № 10. – P. 1693-1704.369.
Yang, W. Application of image correction in 3D reconstruction of mandiblefrom CT slices / W. Yang, J. Liu, M. Liao // Sheng Wu Yi Xue Gong Cheng Xue Za Zhi.– 2004. – Vol. 21, № 3. – P. 387–390.370. Yang, Y. A fast and reliable noise-resistant medical image segmentation andbias field correction model / Y. Yang, D. Tian, B.