Диссертация (1174210), страница 33
Текст из файла (страница 33)
– С. 141–145.130. “Proof-Of-Concept” Evaluation of an Automated Sputum SmearMicroscopy System for Tuberculosis Diagnosis / J. J. Lewis, V. N. Chihota, M. Meulen[et al.] // PLoS One. – 2012. – № 7. – P. 11.131. 3D image analysis and artificial intelligence for bone disease classification /A. Akgundogdu, R. Jennane, G. Aufort [et al.] // J.
Med. Syst. – 2010. – № 34. – P. 815–828.132. A CBCT series slice image segmentation method [Электронный ресурс] /J. Zheng, D. Zhang, K. Huang [et al.] // Xray Sci. Technol. – 2018. – P. 1–18. – Режим230доступа:www.https://content.iospress.com/articles/journal-of-x-ray-science-and-technology/xst180393. – (Дата обращения: 19.08.2018).133. A genetic algorithm-neural network approach for Mycobacteriumtuberculosis detection in Ziehl-Neelsen stained tissue slide images / M. K.
Osman,F. Ahmad, Z. Saad [et al.] // In Proc. of 10th International Conference on IntelligentSystems Design and Applications (ISDA). – 2010. – P. 1229–1234.134. A hybrid seasonal prediction model for tuberculosis incidence in China /S. Cao, F. Wang, W. Tam [et al.] // BMC Med. Inform. Decis. Mak. – 2013. – № 13.
–P. 56.135. A new image correction method for live cell atomic force microscopy /Y. Shen, J. L. Sun, A. Zhang [et al.] // Phys. Med. Biol. – 2007. – Vol. 52, № 8. –P. 2185–2196..136. A Review of Automatic Methods Based on Image Processing Techniquesfor Tuberculosis Detection from Microscopic Sputum Smear Images / R.
O. Panicker,B. Soman, G. Saini [et al.] // J. Med. Syst. – 2016. – Vol. 40, № 1. – P.17.137. A simple image correction method for high-throughput microscopy /A. D. Coster, C. Wichaidit, S. Rajaram [et al.] // Nat. Methods. – 2014. – Vol. 11, № 6.
–P. 602.138. A sputum smear microscopy image database for automatic bacilli detectionin conventional microscopy / M. G. Costa, C. F. F. Costa Filho, K. A. Junior [et al.] // InProc. of 36th Annual International Conference of IEEE Engineering in Medicine andBiology Society (EMBC). – 2014. – P. 2841–2844.139.
A Support Vector Based Fuzzy Neural Network Approach for MassClassification in Mammography, in Digital Signal Processing / F. Moayedi, R. Boostani,Z. Azimifar // 15th International Conference on Digital Signal Processing: Cardiff. –2007. – P. 240–243.140. Asurveyofthresholdingtechniques/P. K. Sahoo,S. Soltani,A. K. C. Wong // Computer Vision, Graphics, and Image Processing. – 1988.
– Vol .41,№ 2. – P .233–260.231141. Active case finding of tuberculosis in Europe: a Tuberculosis NetworkEuropean Trials Group (TBNET) survey / G. H. Bothamley, L. Ditiu, G. B. Migliori [etal.] // Eur. Respir. J. – 2008. – Vol. 32, № 4. – P. 1023–1030.142. Active case finding of tuberculosis: historical perspective and futureprospects / J. E.
Golub, C. I. Mohan, G. W. Comstock [et al.] // Int. J. Tuberc. Lung Dis.– 2005. – Vol. 9, № 11. – P. 1183-1203.143. Active case finding: understanding the burden of tuberculosis in rural SouthAfrica / P. M. Pronyk, B. Joshi, J. R. Hargreaves [et al.] // Int. J. Tuberc. Lung Dis. –2001.
– Vol. 5, № 7. – P. 611–618.144. Agoston, M. K.Computergraphicsandgeometricmodeling:implementation and algoritms / M. K. Agoston. – London: Springer, 2005. – 907 p.145. An Automated Screening System for Tuberculosis / R. Santiago-Mozos,F. Pérez-Cruz, M. G.
Madden [et al.] // IEEE journal of Biomedical and Healthinformatics. – 2014. – Vol. 18, № 3. – P. 855–862.146. An automatic diagnostic system for CT liver image classification /E. L. Chen, P. C. Chung, C. L. Chen [et al.] // IEEE Trans. Biomed. Eng. – 1998. –Vol. 45, № 6. – P. 783-794.147. An estimating equation approach to dimension reduction for longitudinaldata / K. Xu, W.
Guo, M. Xiong [et al.] // Biometrika. – 2016. – № 1. – P. 189–203.148. An image correction protocol to reduce distortion for 3-T stereotactic MRI /W. M. Tavares, F. Tustumi, C. da Costa Leite [et al.] // Neurosurgery. – 2014. – Vol. 74,№ 1. – P. 121–126.149. Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacteriumtuberculosis Infection and Treatment in Non-human Primates / A. G. White, P.
Maiello,M. Coleman // J. Vis. Exp. – 2017. – Vol. 127. – e56375.150. Analysis of adventitious lung sounds originating from pulmonarytuberculosis / K. W. Becker, C. Scheffer, M. M. Blanckenberg [et al.] // Conf. Proc. IEEEEng. Med. Biol. Soc. – 2013. – P. 4334–4337.232151. Anatomy-based registration of CT-scan and intraoperative X-ray images forguiding a surgical robot / A. Guéziec, P.
Kazanzides, B. Williamson [et al.] // IEEE Trans.Med. Imaging. – 1998. – Vol. 17, № 5. – P. 715-728.152. Application of a hybrid model for predicting the incidence of tuberculosis inHubei, China / G. Zhang, S. Huang, Q. Duan [et al.] // PLoS One. – 2013. – Vol. 8, № 11.– e80969.153. Automated detection of tuberculosis in Ziehl-Neelsen stained sputum smearsusing two one-class classifiers / R.
Khutlang, S. Krishnan, A. Whitelaw [et al.] // J.Microsc. – 2010. – Vol. 237, № 1. – P. 96–102.154. Automated fluorescence microscope for tuberculosis detection / K. de Jager,S. Fickling, S. Krishnan [et al.] // Journal of Medical Devices. – 2014. – Vol.
8, № 3.P. 0309431–0309432.155. Automated Identification of Tubercle Bacilli in Sputum: A PreliminaryStudy / K. Veropoulos, G. Learmonth, C. Campbell [et al.] // Analytical and QuantitativeCytology and Histology. – 1999. – Vol. 21, № 4. – P. 277–281.156. Automated image processing method for the diagnosis and classification ofmalaria on thin blood smears / N. E. Ross, C.
J. Pritchard, D. M. Rubin [et al.] // Med.Biol. Eng. Comput. – 2006. – Vol. 44, № 5. – P. 427–436.157. Automated seizure detection using limited-channel EEG and non-lineardimension reduction / J. Birjandtalab, M. Baran Pouyan, D. Cogan [et al.] // Computersin biology and medicine. – 2017. – № 82. – P. 49–58.158. Automated tuberculosis diagnosis using fluorescence images from a mobilemicroscope / J.
Chang, P. Arbeláez, N. Switz [et al.] // Medical image computing andcomputer-assisted intervention. – 2012. – Vol. 15, № 3. – P. 345–352.159. Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA andcolor-based image classification / K. J. AbuHassan, N. M. Bakhori, N. Kusnin [et al.] //Conf.
Proc. IEEE Eng. Med. Biol. Soc. – 2017. – P. 4512–4515.160. Automatic fundus image classification for computer-aided diagnosis / S. Lu,J. Liu, J. H. Lim [et al.] // Conf. Proc. IEEE Eng. Med. Biol. Soc. – 2009. – P. 1453–1456.233161. Automatic Identification of Mycobacterium tuberculosis from ZN-stainedsputum smear: Algorithm and system design / Y. Zhai, Y. Liu, D.
Zhou [et al.] // In Proc.of IEEE International Conference on Robotics and Biomimetics (ROBIO). – 2010. –P. 41–46.162. Automatic identification of Mycobacterium tuberculosis with conventionallight microscopy / M. G. Costa, C. F. F. Costa Filho, J. F. Sena [et al.] // In Proc. of 30thAnnual International IEEE Eng. Med. Biol. Soc. – 2008. – P. 382–385.163. Automatic multiorgan segmentation in CT images of the male pelvis usingregion-specific hierarchical appearance cluster models / B. Zhou, D. Li, P. Zang [et al.] //Med. Phys.
– 2016. – Vol. 43, № 10. – P. 5426.164. Automatic sputum color image segmentation for tuberculosis diagnosis /M. G. Forero-Vargas, E. L. Sierra-Ballen, J. Alvarez-Borrego [et al.] // In Proc. of SPIEAlgorithms and Systems for Optical Information Processing. – 2001. – P. 251–261.165. Ayas, S. Random forest-based tuberculosis bacteria classification in imagesof ZN-stained sputum smear samples / S. Ayas, M. Elinci // SIViP.
– 2014. – Vol. 8, № 1.– P. 49–61.166. Azeroual, A, Afdel K. Fast Image Edge Detection based on Faber SchauderWavelet and Otsu Threshold / A. Azerounal, K. Afdel // Heliyon. – 2018. – Vol. 11,№ 12. – e00485.167. Balyan, M. K. Object image correction using an X-ray dynamical diffractionFraunhofer hologram / M.
K. Balyan // J. Synchrotron Radiat. – 2014. – Vol. 21, № Pt 2.– P. 449–451.168. Bamford, P. Empirical comparison of cell segmentation algorithms using anannotated dataset / P. Bamford // Proceedings of the Institute of Electrical and ElectronicsEngineers (IEEE) International Conference on Image Processing (ICIP 2003). –Rochester: IEEE, 2003. – P. 1073–1076.169. Becker, R.
L. Computer-assisted image classification: use of neuralnetworks in anatomic pathology / R. L. Becker // Cancer Lett. – 1994. – Vol. 77, № 2-3.– P. 111–117.234170. Borsotti, M. Quantitative evaluation of color image segmentation results /M. Borsotti, P. Campadelli, R. Schettini // Pattern Recognition Letters. – 1998. – Vol.
19,№ 8. – P. 741–747.171. Brain activity-based image classification from rapid serial visualpresentation / N. Bigdely-Shamlo, A. Vankov, R. R. Ramirez [et al.] // IEEE Trans.Neural Syst. Rehabil. Eng. – 2008. – Vol. 16, № 5. – P. 432–441.172. Calvarial eosinophilic granuloma: diagnostic models and image featureselection with a neural network / E. Arana, L.