Boundary-based object segmentation in X-ray imaging using spiral search

References

• Galleguillos, C. and Belongie, S. Context based object categorization: a critical survey. Comput. Vis. Image Und., 2010, 114, 712–722. doi: 10.1016/j.cviu.2010.02.004
   Web of Science ®Google Scholar
• Nhon, T. and Benjamin, K. Skeleton search: category-specific object recognition and segmentation using a skeletal shape model. Int. J. Comput. Vision, 2011, 94, 215–240. doi: 10.1007/s11263-010-0412-0
   Web of Science ®Google Scholar
• Liu, Y., Zhang, D., Lu, G. and Ma, W-Y. A survey of content-based image retrieval with high-level semantics. Pattern Recogn., 2007, 40, 262–282. doi: 10.1016/j.patcog.2006.04.045
   Web of Science ®Google Scholar
• Zhang, D., Islam, Md. M. and Lu, G. A review on automatic image annotation techniques. Pattern Recogn., 2012, 45, 346–362. doi: 10.1016/j.patcog.2011.05.013
   Web of Science ®Google Scholar
• Zhang, H., Fritts, J. E. and Goldman, S. A. Image segmentation evaluation: a survey of unsupervised methods. Comput. Image Und., 2008, 110, 260–280. doi: 10.1016/j.cviu.2007.08.003
   Web of Science ®Google Scholar
• Arora, S., Acharya, J., Verma, A. and Panigrahi, P. K. Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recogn. Lett., 2008, 29, 119–125. doi: 10.1016/j.patrec.2007.09.005
   Web of Science ®Google Scholar
• Dirami, A., Hammouche, K., Diaf, M. and Siarry, P. Fast multilevel thresholding for image segmentation through a multiphase level set method. Signal Process., 2013, 93, 139–153. doi: 10.1016/j.sigpro.2012.07.010
   Web of Science ®Google Scholar
• Yu, Z., Au, O. C., Zou, R., Yu, W. and Tian, J. An adaptive unsupervised approach toward pixel clustering and color image segmentation. Pattern Recogn., 2010, 43, 1889–1906. doi: 10.1016/j.patcog.2009.11.015
   Web of Science ®Google Scholar
• Capitaine, H. L. and Frelicot, C. A fast fuzzy C-means algorithm for color image segmentation. In: Proc. of EUSFLAT-LFA, Aix-les-Bains, France, 2011, pp. 1074–1081.
   Google Scholar
• Shih, F. Y. and Cheng, S. Automatic seeded region growing for color image segmentation. Image Vision Comput., 2005, 23, 877–886. doi: 10.1016/j.imavis.2005.05.015
   Web of Science ®Google Scholar
• Espindola, G. M., Camara, G., Reis, I. A., Bins, L. S. and Monteiro, A. M. Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. Int. J. Remote Sens., 2006, 27, 3035–3040. doi: 10.1080/01431160600617194
   Web of Science ®Google Scholar
• Tao, W., Jin, H. and Zhang, Y. Color image segmentation based on mean shift and normalized cuts. IEEE Trans. Syst. Man Cybern. B Cybern, 2007, 37, 1382–1389. doi: 10.1109/TSMCB.2007.902249
   PubMed Web of Science ®Google Scholar
• Ma, W.-Y. and Manjunath, B. S. EdgeFlow: a technique for boundary detection and image segmentation. IEEE Trans. Image Process., 2000, 9, 1375–1388. doi: 10.1109/83.855433
   PubMed Web of Science ®Google Scholar
• Borenstein, E. and Malik, J. Shape guided object segmentation. IEEE Comput. Soc. Conf. on ‘Computer vision and pattern recognition’, New York, NY, USA, 2006, pp. 969–976.
   Google Scholar
• Kim, J. and Grauman, K. Shape sharing for object segmentation. Proc. European Conf. on ‘Computer vision’, Part VII, Florence, Italy, 2012, pp. 444–458.
   Google Scholar
• Meng, F., Li, H., Liu, G. and Ngan, K. N. From logo to object segmentation. IEEE Trans. Multimedia, 2013, 15, 2186–2197. doi: 10.1109/TMM.2013.2280893
   Web of Science ®Google Scholar
• Devaki, K., Murali Bhaskaran, V. and Suphalakshmi, A. Fast watersnakes: an improved image segmentation framework. Imaging Sci. J., 2014, 62, 303–312. doi: 10.1179/1743131X13Y.0000000066
   Web of Science ®Google Scholar
• Munoz-Rodriguez, J. A., Asundi, A. and Rodriguez-Vera, R. Recognition of a light line pattern by Hu moments for 3-D reconstruction of a rotated object. Opt. Laser Technol., 2005, 37, 131–138. doi: 10.1016/j.optlastec.2004.02.019
   Web of Science ®Google Scholar
• Munoz-Rodriguez, J. A. Shape connection by pattern recognition and laser metrology. Appl. Optics, 2008, 47, 3590–3608. doi: 10.1364/AO.47.003590
   PubMed Web of Science ®Google Scholar
• Munoz-Rodriguez, J. A., Rodriguez-Vera, R. and Servin, M. Direct object shape detection based on skeleton extraction of a light line. Opt. Eng., 2000, 39, 2463–2471. doi: 10.1117/1.1287931
   Web of Science ®Google Scholar
• Munoz-Rodriguez, J. A. and Mejia-Alanis, F. Supplying interpolation and continuity in surface modeling via Bezier networks and optical metrology. Opt. Eng., 2014, 53, 092003. doi: 10.1117/1.OE.53.9.092003
   Google Scholar
• Li, J., Randall, J. and Guan, L. Perceptual image processing for digital edge linking. IEEE CCECE (Electrical and Computer Engineering), 2003, 2, 1215–1218.
   Google Scholar
• Pathegama, M. P. and Gol, O. Edge-end pixel extraction for edge-based image segmentation. Int. J. Comput. Elect. Autom. Cont. Inform. Eng., 2007, 1, 453–456.
   Google Scholar
• Sappa, A. D. Unsupervised contour closure algorithm for range image edge-based segmentation. IEEE Trans. Image Process., 2006, 15, 377–384. doi: 10.1109/TIP.2005.860612
   PubMed Web of Science ®Google Scholar
• Wang, Z. and Zhang, H. Edge linking using geodesic distance and neighborhood information. Int. Conf. on ‘Advanced intelligent mechatronics’, Xi'an, China, 2008, pp. 151–155.
   Google Scholar
• Jevtic, A. Ant based edge linking algorithm. Ann. Conf. of ‘IEEE industrial electronics’, Porto, Portugal, 2009, pp. 3353–3358.
   Google Scholar
• Canny, J. F. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 1986, 8, 679–698. doi: 10.1109/TPAMI.1986.4767851
   PubMed Web of Science ®Google Scholar
• Munoz-Rodriguez, J. A. Online self-camera orientation based on laser metrology and computer algorithms. Opt. Commun., 2011, 284, 5601–5612. doi: 10.1016/j.optcom.2011.08.045
   Google Scholar
• Kovesi, P. Edge linking and line segment fitting. Available at http://www.Peterkovesi.com/matlabfns/#edgelink, (accessed June 18, 2016).
   Google Scholar
• Martin, D. R., Fowlkes, C. C. and Malik, J. Learning to detect natural image boundaries using local brightness, color, and texture cue. IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, 530–549. doi: 10.1109/TPAMI.2004.1273918
   PubMed Web of Science ®Google Scholar