An Improved Neuron Segmentation Model for Crack Detection – Image Segmentation Model

1. National Transportation Safety Board, Collapse of 1-35W Highway Bridge, Minneapolis, 1 August 2007. http://www.dot.state.mn.us/i35wbridge/ntsb/finalreport.pdfSearch in Google Scholar

2. Asakura, T., Y. Kojima. Tunnel Maintenance in Japan. – Tunnelling & Underground Space Technology, Vol. 18, 2003, No 2-3, pp. 161-169.10.1016/S0886-7798(03)00024-5Search in Google Scholar

3. Koch, C., K. Georgieva, V. Kasireddy et al. A Review on Computer Vision Based Defect Detection and Condition Assessment of Concrete and Asphalt Civil Infrastructure. – Advanced Engineering Informatics, Vol. 29, 2015, No 2, pp. 196-210.10.1016/j.aei.2015.01.008Search in Google Scholar

4. Zaurin, R., F. N. Catbas. Integration of Computer Imaging and Sensor Data for Structural Health Monitoring of Bridges. – Smart Materials & Structures, Vol. 19, 2009, No 1, pp. 427-437.10.1088/0964-1726/19/1/015019Search in Google Scholar

5. Paden, A., T. Bryant, D. E. Lu et al. Supervised Computer-Vision-Based Sensing of Concrete Bridges for Crack-Detection and Assessment. – Transportation Research Board Annual Meeting, 2014.Search in Google Scholar

6. Jahanshahi, M. R., J. S. Kelly, S. F. Masri et al. A Survey and Evaluation of Promising Approaches for Automatic Image-Based Defect Detection of Bridge Structures. – Structure & Infrastructure Engineering Maintenance, Vol. 5, 2009, No 6, pp. 455-486.10.1080/15732470801945930Search in Google Scholar

7. Abdelqader, I., O. Abudayyeh, M. E. Kelly. Analysis of Edge-Detection Techniques for Crack Identification in Bridges. – Journal of Computing in Civil Engineering, Vol. 17, 2014, No 4, pp. 255-263.10.1061/(ASCE)0887-3801(2003)17:4(255)Search in Google Scholar

8. Non-Member T. Y., Non-Member S. N., Non-Member R. S. et al. Image-Based Crack Detection for Real Concrete Surfaces. – IEEE Transactions on Electrical & Electronic Engineering, Vol. 3, 2008, No 1, pp. 128-135.10.1002/tee.20244Search in Google Scholar

9. Yamaguchi, T., S. Hashimoto. Fast Crack Detection Method for Large-Size Concrete Surface Images Using Percolation-Based Image Processing. – Machine Vision & Applications, Vol. 21, 2010, No 5, pp. 797-809.10.1007/s00138-009-0189-8Search in Google Scholar

10. Koch, C., S. G. Paal, A. Rashidi et al. Achievements and Challenges in Machine Vision-Based Inspection of Large Concrete Structures. – Advances in Structural Engineering, Vol. 17, 2014, No 3, pp. 303-318.10.1260/1369-4332.17.3.303Search in Google Scholar

11. Sinha, S. K., P. W. Fieguth. Automated Detection of Cracks in Buried Concrete Pipe Images. – Automation in Construction, Vol. 15, 2006, No 1, pp. 58-72.10.1016/j.autcon.2005.02.006Search in Google Scholar

12. Sinha, S. K., P. W. Fieguth. Morphological Segmentation and Classification of Underground Pipe Images. – Machine Vision & Applications, Vol. 17, 2006, No 1, pp. 21-31.10.1007/s00138-005-0012-0Search in Google Scholar

13. Fujita, Y., Y. Hamamoto. A Robust Automatic Crack Detection Method from Noisy Concrete Surfaces. – Machine Vision & Applications, Vol. 22, 2011, No 2, pp. 245-254.10.1007/s00138-009-0244-5Search in Google Scholar

14. Abdelqader, I., O. Abudayyeh, M. E. Kelly. Analysis of Edge-Detection Techniques for Crack Identification in Bridges. – Journal of Computing in Civil Engineering, Vol. 17, 2014, No 4, pp. 255-263.10.1061/(ASCE)0887-3801(2003)17:4(255)Search in Google Scholar

15. Chambon, S., P. Subirats, J. Dumoulin. Introduction of a Wavelet Transform Based on 2D Matched Filter in a Markov Random Field for Fine Structure Extraction: Application on Road Crack Detection. – In: Proc. of SPIE – The International Society for Optical Engineering, 2009.Search in Google Scholar

16. Li, Q., Q. Zou, D. Zhang. FoSA: F* Seed-Growing Approach for Crack-Line Detection from Pavement Images. – Image & Vision Computing, Vol. 29, 2011, No 12, pp. 861-872.10.1016/j.imavis.2011.10.003Search in Google Scholar

17. Nguyen, T. S., S. Begot, F. Duculty et al. Free-Form Anisotropy: A New Method for Crack Detection on Pavement Surface Images. – In: 18th IEEE International Conference on Image Processing (ICIP), 2011, pp. 1069-1072.Search in Google Scholar

18. Abdel-Qader, I., S. Pashaie-Rad, O. Abudayyeh, S. Yehia. PCA-Based Algorithm for Unsupervised Bridge Crack Detection. – Adv. Engineering Software, Vol. 37, 2006, No 12, pp. 71-778.10.1016/j.advengsoft.2006.06.002Search in Google Scholar

19. Prasanna, P., K. Dana, N. Gucunski et al. Computer-Vision Based Crack Detection and Analysis. – Sensors & Smart Structures Technologies for Civil Mechanical & Aerospace Systems, 2012, 8345.10.1117/12.915384Search in Google Scholar

20. Lattanzi, D., G. R. Miller. Robust Automated Concrete Damage Detection Algorithms for Field Applications. – Journal of Computing in Civil Engineering, Vol. 28, 2014, No 2, pp. 253-262.10.1061/(ASCE)CP.1943-5487.0000257Search in Google Scholar

21. Yu, S. N., J. H. Jang, C. S. Han. Auto Inspection System Using a Mobile Robot for Detecting Concrete Cracks in a Tunnel. – Automation in Construction, Vol. 16, 2007, No 3, pp. 255-261.10.1016/j.autcon.2006.05.003Search in Google Scholar

22. Gunkel, C., A. Stepper, A. C. Müller et al. Micro Crack Detection with Dijkstra’s Shortest Path Algorithm. – Machine Vision & Applications, Vol. 23, 2012, No 3, pp. 589-601.10.1007/s00138-011-0324-1Search in Google Scholar

23. Vivek, K., Y. Anthony, J. T. Yichang. Detecting Curves with Unknown Endpoints and Arbitrary Topology Using Minimal Paths. – IEEE Transactions on Software Engineering, Vol. 36, 2011, No 3, pp. 1952-1965.10.1109/TPAMI.2011.26722201054Search in Google Scholar

24. Tsai, Y., V. Kaul, A. Yezzi. Automating the Crack Map Detection Process for Machine Operated Crack Sealer. – Automation in Construction, Vol. 31, 2013, No 3, pp. 10-18.10.1016/j.autcon.2012.11.033Search in Google Scholar

25. Zhao, G., T. Wang, J. Ye. Surface Shape Recognition Method for Crack Detection. – Journal of Electronic Imaging, Vol. 23, 2014, No 3, pp. 6-8.10.1117/1.JEI.23.3.033013Search in Google Scholar

26. Zou, Q., Y. Cao, Q. Li et al. Crack Tree: Automatic Crack Detection from Pavement Images. – Pattern Recognition Letters, Vol. 33, 2012, No 3, pp. 227-238.10.1016/j.patrec.2011.11.004Search in Google Scholar

27. Xie, J., T. Zhao, T. Lee et al. Anisotropic Path Searching for Automatic Neuron Reconstruction. – Medical Image Analysis, Vol. 15, 2011, No 5, pp. 680-689.10.1016/j.media.2011.05.013Search in Google Scholar

28. Peng, H., F. Long, G. Myers. Automatic 3D Neuron Tracing Using All-Path Pruning. – Bioinformatics, Vol. 27, 2011, No 13, pp. i239-i247.10.1093/bioinformatics/btr237Search in Google Scholar

29. Dijkstra, B. E. A Note on Two Problems in Connection with Graphs. – Numerische Mathematics, Vol. 1, 1959, No 1, pp. 269-271.10.1007/BF01386390Search in Google Scholar

30. Jahanshahi, M. R., S. F. Masri, C. W. Padgett et al. An Innovative Methodology for Detection and Quantification of Cracks through Incorporation of Depth Perception. – Machine Vision & Applications, Vol. 24, 2013, No 2, pp. 227-241.10.1007/s00138-011-0394-0Search in Google Scholar

31. Jahanshahi, M. R., S. F. Masri. Adaptive Vision-Based Crack Detection Using 3D Scene Reconstruction for Condition Assessment of Structures. – Automation in Construction, Vol. 22, 2012, pp. 567-576.10.1016/j.autcon.2011.11.018Search in Google Scholar

32. Torok, M. M., M. Golparvar-Fard, K. B. Kochersberger. Image-Based Automated 3D Crack Detection for Post-Disaster Building Assessment. – Journal of Computing in Civil Engineering, Vol. 28, 2013.10.1061/(ASCE)CP.1943-5487.0000334Search in Google Scholar

33. Mukherjee, S., C. Barry, S. T. Acton. Tubularity Flow Field – A Technique for Automatic Neuron Segmentation. – IEEE Transactions on Image Processing, Vol. 24, 2015, No 1, pp. 374-389.10.1109/TIP.2014.237805225494506Search in Google Scholar

34. Al-Kofahi, K. A., C. Ali, L. Sharie et al. Median-Based Robust Algorithms for Tracing Neurons from Noisy Confocal Microscope Images. – IEEE Transactions on Information Technology in Biomedicine, Vol. 7, 2003, No 4, pp. 302-317.10.1109/TITB.2003.816564Search in Google Scholar

35. Rodriguez, A., D. B. Ehlenberger, P. R. Hof et al. Three-Dimensional Neuron Tracing by Voxel Scooping. – Journal of Neuroscience Methods, Vol. 184, 2009, No 1, pp. 169-175.10.1016/j.jneumeth.2009.07.021Search in Google Scholar

36. Wearne, S. L., A. Rodriguez, D. B. Ehlenberger et al. New Techniques for Imaging, Digitization and Analysis of Three-Dimensional Neural Morphology on Multiple Scales. – Neuroscience, Vol. 136, 2005, No 3, pp. 661-680.10.1016/j.neuroscience.2005.05.053Search in Google Scholar

37. Cuntz, H., F. Forstner, J. Haag et al. The Morphological Identity of Insect Dendrites. – Plos Computational Biology, Vol. 4, 2009, No 12, pp. 1271-1276.10.1371/journal.pcbi.1000251Search in Google Scholar

38. Mukherjee, S., S. T. Acton. Vector Field Convolution Medialness Applied to Neuron Tracing. – In: 20th IEEE International Conference on Image Processing (ICIP’13), 2013, IEEE, pp. 665-669.10.1109/ICIP.2013.6738137Search in Google Scholar

39. Basu, S., B. Condron, A. Aksel et al. Segmentation and Tracing of Single Neurons from 3D Confocal Microscope Images. – IEEE Transactions on Information Technology in Biomedicine, Vol. 17, 2012, No 2, pp. 319-335.10.1109/TITB.2012.2209670Search in Google Scholar

40. Mukherjee, S., S. Basu, B. Condron et al. Tree2Tree2: Neuron Tracing in 3D. – International Symposium on Biomedical Imaging, 2013, pp. 448-451.10.1109/ISBI.2013.6556508Search in Google Scholar

41. Vese, L. A., T. F. Chan. A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model. – International Journal of Computer Vision, Vol. 50, 2002, No 3, pp. 271-293.10.1023/A:1020874308076Search in Google Scholar

42. Zhao, H. K., T. Chan, B. Merriman et al. A Variational Level Set Approach to Multiphase Motion. – Journal of Computational Physics, Vol. 127, 1996, No 1, pp. 179-195.10.1006/jcph.1996.0167Search in Google Scholar

43. Otsu, N. A Threshold Selection Method from Gray-Level Histograms. – Automatica, Vol. 11, 1975, Nos. 285-296, pp. 23-27.Search in Google Scholar

44. Acton, S. T. Fast Algorithms for Area Morphology. – Digital Signal Processing, Vol. 11, 2001, No 3, pp. 187-203.10.1006/dspr.2001.0386Search in Google Scholar

45. Jacob, M., M. Unser. Design of Steerable Filters for Feature Detection Using Canny-Like Criteria. – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, 2004, No 8, pp. 1007-1019.10.1109/TPAMI.2004.44Search in Google Scholar

46. Stanley, O., J. A. Sethian. Fronts Propagating with Curvature-Dependent Speed: Algorithms Based on Halmiton-Jacobi Formulations. – Journal of Computational Physics, Vol. 79, 1988, No 1, pp. 12-49.10.1016/0021-9991(88)90002-2Search in Google Scholar