Selection of suitable segmentation technique based on image quality metrics

References

• Sridhar S. Digital image processing. India: Oxford; 2011.
   Google Scholar
• Zaitoun NM, Aqel MJ. Survey on image segmentation techniques. Int Conf Commun Manag Inform Technol. 2015;65:797–806.
   Google Scholar
• Gonzalez RG, Woods RE. Third edition digital image processing. Upper Saddle River (NJ): Pearson; 2014.
   Google Scholar
• Semmlow JL. Biosignal and medical image processing. New York (NY): CRC Press; 2009.
   Google Scholar
• Bhargavi K, Jyothi S. A survey on threshold based segmentation technique in image processing. Int J Innov Res Dev. 2014;3(12):234–239.
   Google Scholar
• Najarian K, Splinter R. Biomedical signal and image processing. New York (NY): Taylor & Francis; 2006.
   Google Scholar
• Alheritiere H, Cloppet F, Kurtz C, et al. A document straight line based segmentation for complex layout extraction. 14th IAPR International Conference on Document Analysis and Recognition; 2017. p. 1126–1131.
   Google Scholar
• Vijayalakshmi S, Dahiya S. A review and proposal of an innovative approach for medical image segmentation. International Conference on Computing, Communication and Automation (ICCCA2017); 2017; IEEE. p. 1167–1172.
   Google Scholar
• Padmapriya B, Kesavamurthi T, Wassim Ferose H. Edge based image segmentation technique for detection and estimation of the bladder wall thickness. International Conference on Communication Technology and System Design; 2012. p. 828–835.
   Google Scholar
• Bala Krishnan K, Ranga SP, Guptha N. A survey on different edge detection techniques for image segmentation. Indian J Sci Technol. January 2017;10(4):1–8. DOI:10.17485/ijst/2017/v10i4/108963 doi: 10.17485/ijst/2017/v10i19/107654
   Google Scholar
• Sameena S, Anitha T. A remote sensing image segmentation method based on spectral and texture information. Int J Innov Res Electron Commun (IJIREC). 2015;2(8):28–36.
   Google Scholar
• Sun Q, Cai J, Sun Z. Detection of surface defects on steel strips based on singular value decomposition of digital image. Math Probl Eng. 2016;2016:5797654.
   Google Scholar
• Sachan P, Khanna P. Foreground segmentation and change detection using singular value decomposition. IOSR J Eng. 2019;9(5):12–18.
   Google Scholar
• Kaur D, Kaur Y. Various image segmentation techniques: a review. Int J Comput Sci Mob Comput. 2014;3(5):809–814.
   Google Scholar
• Kaushik K, Rakesh Kumar K, Suresha D. Segmentation of the white matter from the brain fMRI images. Int J Advan Res Comput Eng Technol (IJARCET). 2013;2(4):1314–1317.
   Google Scholar
• Wang W, Duan L, Wang Y. Fast image segmentation using two-dimensional otsu based on estimation of distribution algorithm. J Electr Comput Eng. 2017;2017:1735176.
   Google Scholar
• Vala HJ, Baxi A. A review on otsu image segmentation algorithm. Int J Advan Res Comput Eng Technol (IJARCET). 2013;2(2):387–389.
   Google Scholar
• Kavita RS, Bala R, Siwach S. Review paper on overview of image processing and image segmentation. Int J Res Comput Appl Robot. 2013;1(7):1–13.
   Google Scholar
• Huang G, Pun C-M. Interactive segmentation based on initial segmentation and region Merging; 2013; IEEE. p. 52–55.
   Google Scholar
• Lukes T, Fliegel K, Klima M. Performance evaluation of image quality metrics with respect to their use for super-resolution enhancement. Fifth International Workshop on quality of Multimedia Experience (QoMEX); 2013. p. 42–43.
   Google Scholar
• Nijm GM, Swiryn S, Larson AC, et al. Evaluation of image quality metrics for comparison of synchronization algorithms for cardiac cine Mri. International Conference on Image Processing; 2008; IEEE. p. 2260–2263.
   Google Scholar
• Mustafa ID, Hassan MA. A comparison between different segmentation techniques used in medical imaging. Am J Biomed Eng. 2016;6:59–69.
   Google Scholar
• Demirkaya O, Asyali MH, Sahoo PK. Image processing with MATLAB® applications in medicine and biology. New York (NY): CRC Press; 2009.
   Google Scholar