Automatic Inhibition Zone Diameter Measurement for Disc Diffusion Test Using Image Segmentation

References

• M. E. A. de Kraker, A. J. Stewardson, and S. Harbarth, “Will 10 million people die a year due to antimicrobial resistance by 2050?” PLoS Med., Vol. 13, no. 11, Nov. 2016, doi:10.1371/journal.pmed.1002184.
   PubMed Web of Science ®Google Scholar
• L. F. Costa, E. S. da Silva, V. T. Noronha, I. Vaz-Moreira, O. C. Nunes, and M. M. de Andrade, “Development of an automatic identification algorithm for antibiogram analysis,” Comput. Biol. Med., vol. 67, pp. 104–15, 2015, doi:10.1016/j.compbiomed.2015.09.020.
   PubMed Web of Science ®Google Scholar
• C. A. Alonso et al., “Antibiogramj: a tool for analysing images from disk diffusion tests,” Comput. Methods Programs Biomed., Vol. 143, pp. 159–69, 2017, doi:10.1016/j.cmpb.2017.03.010.
   PubMed Web of Science ®Google Scholar
• M. Pascucci et al., “The first AI-based mobile application for antibiotic resistance testing,” pp. 1–34, 2020, doi:10.1101/2020.07.23.216929.
   Google Scholar
• G. Hejblum, V. Jarlier, J. Grosset, and A. Aurengo. “Automated interpretation of disk diffusion antibiotic susceptibility tests with the radial profile analysis algorithm,” 1993.
   Google Scholar
• E. A. Idelevich, K. Becker, J. Schmitz, D. Knaack, G. Peters, and R. Köck, “Evaluation of an automated system for reading and interpreting disk diffusion antimicrobial susceptibility testing of fastidious bacteria,” PLoS One, Vol. 11, no. 7, 2016. doi:10.1371/journal.pone.0159183.
   Web of Science ®Google Scholar
• E. S. Lestari et al., “Antimicrobial resistance among commensal isolates of escherichia coli and Staphylococcus aureus in the Indonesian population inside and outside hospitals,” Eur. J. Clin. Microbiol. Infect. Dis., Vol. 27, no. 1, pp. 45–51, Jan. 2008, doi:10.1007/s10096-007-0396-z.
   PubMed Web of Science ®Google Scholar
• J. M. Andrews, F. J. Boswell, and R. Wise, “Evaluation of the Oxoid Aura image system for measuring zones of inhibition with the disc diffusion technique,” J. Antimicrob. Chemother., vol. 46, no. 4, pp. 535–40, Oct. 2000, doi:10.1093/jac/46.4.535.
   PubMed Web of Science ®Google Scholar
• L. Salgado, J. M. Menéndez, E. Rendón, N. Garciá, and V. Ruiz, “Automatic antibiograms inhibition halo determination through texture and directional filtering analysis,” IEEE International Conference on Image Processing, Vol. 2, pp. 629–32, 2001. doi:10.1109/icip.2001.958572.
   Google Scholar
• H. S. Bhargav, S. D. Shastri, S. P. Poornav, K. M. Darshan, and M. M. Nayak. “Measurement of the zone of inhibition of an antibiotic,” in Proceedings – 6th International Advanced Computing Conference, IACC 2016, Aug. 2016, pp. 409–414, doi:10.1109/IACC.2016.82.
   Google Scholar
• A. van Belkum, C. A. D. Burnham, J. W. A. Rossen, F. Mallard, O. Rochas, and W. M. Dunne, “Innovative and rapid antimicrobial susceptibility testing systems,” Nat. Rev. Microbiol., Vol. 18, no. 5, pp. 299–311, May 01, 2020. doi:10.1038/s41579-020-0327-x.
   PubMed Web of Science ®Google Scholar
• J. Hudzicki. “Kirby-Bauer disk diffusion susceptibility test protocol,” Dec. 2009, Accessed: Nov. 4, 2020. [Online]. Available: https://www.asmscience.org/content/education/protocol/protocol.3189.
   Google Scholar
• L. Cyr and K. Francis, “Measures of clinical agreement for nominal and categorical data: the kappa coefficient,” Comput. Biol. Med., Vol. 22, no. 4, pp. 239–46, Jul. 1992, doi: 10.1016/0010-4825(92)90063-S.
   PubMed Web of Science ®Google Scholar
• S. Sharma, P. Kumar, and K. Kumar. “A-PNR: automatic plate number recognition,” in Proceedings of the 7th International Conference on Computer and Communication Technology, 2017, pp. 106–110, doi: 10.1145/3154979.3154999.
   Google Scholar
• K. Kumar, S. Sinha, and P. Manupriya. “D-PNR: deep license plate number recognition,” in Proceedings of 2nd International Conference on Computer Vision & Image Processing, 2018, pp. 37–46.
   Google Scholar
• K. K. Singh, M. K. Bajpai, R. K. Pandey, and P. Munshi, “A novel non-invasive method for extraction of geometrical and texture features of wood,” Res. Nondestruct. Eval., Vol. 28, no. 3, pp. 150–67, 2017.
   Web of Science ®Google Scholar
• W. Mokrzycki, and M. Samko, “Canny edge detection algorithm modification,” Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Vol. 7594 LNCS, pp. 533–40, 2012. doi: 10.1007/978-3-642-33564-8_64.
   Google Scholar
• D. Kruk, A. Boucher, A. Lalande, A. Cochet, and T. Sliwa, “Segmentation integrating watershed and shape priors applied to cardiac delayed enhancement MR images,” IRBM, Vol. 38, no. 4, pp. 224–7, Aug. 2017, doi: 10.1016/j.irbm.2017.06.004.
   Web of Science ®Google Scholar
• P. Smith, D. B. Reid, C. Environment, L. Palo, P. Alto, and P. L. Smith, “NOBUYUKI OTSU. – 1979 – a tlreshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern., Vol. 20, no. 1, pp. 62–6, 1979.
   Google Scholar
• F. Lecellier, S. Jehan-Besson, and J. Fadili, “Statistical region-based active contours for segmentation: an overview,” IRBM, Vol. 35, no. 1, pp. 3–10, Feb. 2014. doi: 10.1016/j.irbm.2013.12.002.
   Web of Science ®Google Scholar
• T. F. Chan, and L. A. Vese. “Active Contours Without Edges,” 2001.
   Google Scholar
• P. Getreuer, “Chan-vese segmentation,” Image Process. Line, Vol. 2, pp. 214–24, 2012. doi: 10.5201/ipol.2012.g-cv.
   Google Scholar
• Q. Mahmood, A. Chodorowski, and M. Persson, “Automated MRI brain tissue segmentation based on mean shift and fuzzy c -means using a priori tissue probability maps,” IRBM, Vol. 36, no. 3, pp. 185–96, Jun. 2015. doi: 10.1016/j.irbm.2015.01.007.
   Web of Science ®Google Scholar
• A. Baâzaoui, W. Barhoumi, A. Ahmed, and E. Zagrouba, “Semi-automated segmentation of single and multiple tumors in liver CT images using entropy-based fuzzy region growing,” IRBM, Vol. 38, no. 2, pp. 98–108, Apr. 2017. doi:10.1016/j.irbm.2017.02.003.
   Web of Science ®Google Scholar
• B. N. Li, C. K. Chui, S. Chang, and S. H. Ong, “Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation,” Comput. Biol. Med., Vol. 41, no. 1, pp. 1–10, 2011, doi:10.1016/j.compbiomed.2010.10.007.
   PubMed Web of Science ®Google Scholar
• A. Mishra, K. Kumar, P. Kumar, and P. Mittal. “A novel approach for handwritten character recognition using K-NN classifier,” in Soft Computing: Theories and Applications, Springer, 2020, pp. 887–894.
   Google Scholar
• X. Wu, S. A. Spencer, S. Shen, J. B. Fiveash, J. Duan, and I. A. Brezovich, “Development of an accelerated GVF semi-automatic contouring algorithm for radiotherapy treatment planning,” Comput. Biol. Med., Vol. 39, no. 7, pp. 650–6, 2009, doi:10.1016/j.compbiomed.2009.05.001.
   PubMed Web of Science ®Google Scholar
• Y. Chen et al., “Using prior shapes in geometric active contours in a variational framework,” Int. J. Comput. Vis., Vol. 50, no. 3, pp. 315–28, 2002, doi:10.1023/A:1020878408985.
   Web of Science ®Google Scholar
• C. Li, C. Xu, C. Gui, and M. D. Fox, “Level set evolution without re-initialization: a new variational formulation,” Proc. – 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, CVPR 2005, Vol. I, pp. 430–6, 2005. doi:10.1109/CVPR.2005.213.
   Google Scholar
• P. R. Bai, Q. Y. Liu, L. Li, S. H. Teng, J. Li, and M. Y. Cao, “A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation,” Comput. Biol. Med., Vol. 43, no. 11, pp. 1827–32, Nov. 2013, doi:10.1016/j.compbiomed.2013.08.024.
   PubMed Web of Science ®Google Scholar
• J. Benesty, J. Chen, Y. Huang, and I. Cohen. Pearson Correlation Coefficient, Springer, Berlin, Heidelberg, 2009, pp. 1–4.
   Google Scholar
• S. R. Collins, M. Schuldiner, N. J. Krogan, and J. S. Weissman, “A strategy for extracting and analyzing large-scale quantitative epistatic interaction data,” Genome Biol., Vol. 7, no. 7, pp. 1–14, Jul. 2006, doi: 10.1186/gb-2006-7-7-r63.
   Web of Science ®Google Scholar
• A. Nijs, R. Cartuyvels, A. Mewis, V. Peeters, J. L. Rummens, and K. Magerman, “Comparison and evaluation of Osiris and Sirscan 2000 antimicrobial susceptibility systems in the clinical microbiology laboratory,” J. Clin. Microbiol., Vol. 41, no. 8, pp. 3627–30, Aug. 2003, doi: 10.1128/JCM.41.8.3627-3630.2003.
   PubMed Web of Science ®Google Scholar