Estimation of Optimal Range for Shape From Focus in Microscopic System

Abstract

Shape From Focus (SFF) is one of the most preferred approaches in microscopic systems to reconstruct the 3D shape of the specimen. Classical approach generates a 3D shape using the 2D image sequence with the same field of perspective and different focused regions. In order to scan the specimen in traditional SFF approaches, the microscope platform is moved along the specified range, which is randomly defined between the begin and end locations on the Z axis. A certain amount of 2D image sequence with the same field of perspective and different focused regions are acquired in limited movements between these locations. However, the range and amount of 2D image sequence are effective to extract the entire 3D structure of the specimen, they must be optimized for the type of the examined specimen and magnification objective. In this study, a novel SFF approach is improved to scan the whole 3D structure of the specimen by estimating the optimal range between the begin and end locations of microscope platform. Unlike previous approaches, problems such as random amount of 2D image sequence with the same field of perspective and different focused regions, random determination of the range between the begin and end locations and the occurrence of outliers and noise around the 3D shape are solved in the proposed approach. Our experiments are performed on data sets collected from the light microscopy using specimen prepared for cytopathological examination. Qualitative and quantitative results demonstrate that better performance is achieved by our suggested SFF approach.

Keywords

• Focus measurement operator
• Shape from focus
• 3D shape reconstruction

References

1. [1] B. Billiot, F. Cointault, L. Journaux, J. C. Simon, P. Gouton, “3D Image Acquisition System Based on Shape from Focus Technique”, Sensors, Vol. 13, No. 4, pp. 5040-5053, 2013.
2. [2] S. Pertuz, D. Puig, M A. Garcia, “Analysis of focus measure operators for shape-from-focus”, Pattern Recognition, Vol. 46, No. 5, pp. 1415-1432, 2013.
3. [3] S. O. Shim, A. S. Malik, T. S. Choi, “Accurate shape from focus based on focus adjustment in optical microscopy”, Microscopy Research and Technique, Vol. 72, No. 5, pp. 362-370, 2009.
4. [4] J. M. Geusebroek, F. Cornelissen, A. W. Smeulders, H. Geerts, “Robust autofocusing in microscopy”, Cytometry: The Journal of the International Society for Analytical Cytology, Vol. 39, No. 1, pp. 1-9, 2000.
5. [5] A. S. Malik, T. S. Choi, “A novel algorithm for estimation of depth map using image focus for 3d shape recovery in the presence of noise”, Pattern Recognition, Vol. 41, No. 7, pp. 2200-2225, 2008.
6. [6] M. B. Ahmad, T. S. Choi, “Application of three dimensional shape from image focus in lcd/tft displays manufacturing”, IEEE Transactions on Consumer Electronics, Vol. 53, No. 1, pp. 1-4, 2007.
7. [7] Y. An, G. Kang, I. J. Kim, H. S. Chung, J. Park, “Shape from focus through laplacian using 3d window”, Second International Conference on Future Generation Communication and Networking, pp. 46-50, December 2008.
8. [8] J. L. Pech Pacheco, G. Cristobal, J. Chamorro Martinez, J. Fernandez Valdivia, “Diatom autofocusing in brightfield microscopy: a comparative study”, Proceedings 15th International Conference on Pattern Recognition, pp. 314-317, 2000.

9. [9] T. Yan, Z. Hu, Y. Qian, Z. Qiao, L. Zhang, “3D shape reconstruction from multifocus image fusion using a multidirectional modified Laplacian operator”, Pattern Recognition, Vol. 98, 107065, 2020.
10. [10] S. Nayar, Y. Nakagawa, “Shape from focus: an effective approach for rough surfaces”, IEEE International Conference on Robotics and Automation, 1990, pp. 218-225, 1990.
11. [11] C. Y. Wee, R. Paramesran, “Measure of image sharpness using eigenvalues”, Information Sciences, Vol. 177, No. 12, pp. 2533-2552, 2007.
12. [12] P. T. Yap, P. Raveendran, “Image focus measure based on chebyshev moments”, IEE Proceedings-Vision, Image and Signal Processing, Vol. 151, No. 2, pp. 128-136, 2004.
13. [13] S. Y. Lee, Y. Kumar, J. M. Cho, S. W. Lee, S. W. Kim, “Enhanced autofocus algorithm using robust focus measure and fuzzy reasoning”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 18, No. 9, pp. 1237-1246, 2008.
14. [14] S. Y. Lee, J. T. Yoo, Y. Kumar, S. W. Kim, “Reduced energy-ratio measure for robust autofocusing in digital camera”, IEEE Signal Processing Letters, Vol. 16, No. 2, pp. 133-136, 2009.
15. [15] C. H. Shen, H H., Chen, “Robust focus measure for low-contrast images”, Digest of Technical Papers International Conference on Consumer Electronics, pp. 69-70, 2006.
16. [16] U. Ali, M. T. Mahmood, “3d shape recovery by aggregating 3d wavelet transform-based image focus volumes through 3d weighted least squares”, Journal of Mathematical Imaging and Vision, Vol. 62, No. 1, pp. 1-19, 2019.
17. [17] H. Xie, W. Rong, L. Sun, “Wavelet-based focus measure and 3-d surface reconstruction method for microscopy images”, IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 229-234, 2006.
18. [18] F. S. Helmli, S. Scherer, “Adaptive shape from focus with an error estimation in light microscopy”, Image and Signal Processing and Analysis, pp. 188-193, 2001.
19. [19] M. V. Shirvaikar, “An optimal measure for camera focus and exposure”, Proceedings of the Thirty-Sixth South eastern Symposium, pp. 472-475, 2004.
20. [20] H. Nanda, R. Cutler, “Practical calibrations for a real-time digital omnidirectional camera”, CVPR Technical Sketch, Vol. 20, No. 2, 2001.
21. [21] J. Lorenzo, M. Castrillon, J. Mendez, O. Deniz, “Exploring the use of local binary patterns as focus measure”, Computational Intelligence for Modelling Control Automation, pp. 855-860, 2008.
22. [22] R. Minhas, A. Adeel Mohammed, M. Q. Jonathan Wu, “Shape from focus using fast discrete curvelet transform”, Pattern, Recognition, Vol. 44, No. 4, pp. 839-853, 2011.
23. [23] F. Mahmood, J. Mahmood, A. Zeb, J. Iqbal, “3d shape recovery from image focus using gabor features”, Tenth International Conference on Machine Vision, 2017.
24. [24] T. Fan, H. Yu, “A novel shape from focus method based on 3d steerable Filters for improved performance on treating textureless region”, Optics Communications, Vol. 410, pp. 254-261, 2018.
25. [25] S. Pertuz, D. Puig, M. A. Garcia, “Reliability measure for shape-from-focus”, Image and Vision Computing, Vol. 31, No. 10, pp. 725-734, 2013.
26. [26] I. Lee, M. T. Mahmood, T. S. Choi, “Adaptive window selection for 3D shape recovery from image focus”, Optics Laser Technology, Vol. 45, pp. 21-31, 2013.
27. [27] M. Muhammad, T. S. Choi, “Sampling for shape from focus in optical microscopy”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 3, pp. 564-573, 2012.
28. [28] C. Y. Tseng, S. J. Wang, “Shape-from-focus depth reconstruction with a spatial consistency model”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 24, No. 12, pp. 2063-2076, 2014.
29. [29] W. Liu, X. W. Key, “Semi-global depth from focus”, 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 624-629, 2015.
30. [30] D. C. Tsai, H. H. Chen, “Focus profile modeling”, IEEE Transactions on Image Processing, Vol. 25, No. 2, pp. 818-828, 2016.
31. [31] M. J. Muhammad, T. S. Choi, “Sampling for shape from focus in optical microscopy”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 3, pp. 564-573, 2012.