A Microscopic Image Sharpness Metric Based on the Local Binary Pattern (LBP)

Abstract:

Microscopic image sharpness metric is very sensitive to illumination changes, so the research of sharpness metric methods which have better robustness to illumination is required. A new method which utilized the specialty of different orientation of Local Binary Pattern (LBP) is proposed. Firstly, the effective information and judge orientation of them at fixed region of the image is extracted. Secondly, different connected pattern is adopted to deal with different orientation region and the most robust LBP image is gotten. Finally, the sharpness metric is measured from the LBP image. The experiment results show that this method can inhibit the influence of illumination to microscopic image sharpness metric, and the stability can be improved by 83.89% averagely and 99.90% maximum.

• Periodical: Advanced Materials Research (Volume 902)
• Pages: 330-335
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.902.330
• Online since: February 2014
• Authors: Jian Ning Lai, Yi Gang Wang*, Sheng Li Fan
• Keywords: Illumination Invariant, LBP, Microscopic Image, Sharpness Metric

References

[1] Yu Sun, Stefan Duthaler and Bradley J. Nelson. Autofocusing Algorithm Selection in Computer Microscopy[C], 2005 IEEE International Conference on Intelligent Robots and Systems, (2005), 70 – 76.

DOI: 10.1109/iros.2005.1545017

[2] M. Rahman, N. Kehtarnavaz, S. Yousefi. An Auto-Focus Sharpness Function for Stereo Image Pairs[C], 2011 18th IEEE International Conference on Image Processing, (2011), 633-636.

DOI: 10.1109/icip.2011.6116631

[3] Jose Maria Mateos-Perez, Rafael Redondo, Rodrigo Nava, et al. Comparative Evaluation of Autofocus Algorithms for a Real-Time System for Automatic Detection of Mycobacterium tuberculosis[J], Journal of the International Society for Advancement of Cytometry, (2012).

DOI: 10.1002/cyto.a.22020

[4] O.A. OSIBOTE, R. DENDERE, S. KRISHNAN, et al. Automated Focusing in Bright-field Microscopy for Tuberculosis Detection[J], Journal of Microscopy, (2010), 155–163.

DOI: 10.1111/j.1365-2818.2010.03389.x

[5] Diansheng Cao, Yunguo Gao, Huanli Li, et al. Auto-focusing Evaluation Functions in Digital Image System[C], 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), (2010), V5-331 - V5-334.

DOI: 10.1109/icacte.2010.5579520

[6] Rony Ferzli, Lina J. Karam. A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)[J], IEEE Transactions On Image Processing, (2009), 18(4): 717-728.

DOI: 10.1109/tip.2008.2011760

[7] Dashan Gao, Dirk Padfield, Jens Rittscher, et al. Automated Training Data Generation for Microscopy Focus Classification[J], Medical Image Computing and Computer-Assisted Intervention–MICCAI 2010Lecture Notes in Computer Science, (2010).

DOI: 10.1007/978-3-642-15745-5_55

[8] Weigang Zhang, Chunxi Liu, Qingming Huang, et al. Coarse-to-Fine Dissolve Detection Based on Image Quality Assessment[J], The Era of Interactive Media, (2013), 277-287.

DOI: 10.1007/978-1-4614-3501-3_23

[9] R. Ferzli, Lina J. Karam. No-Reference Objective Wavelet Based Noise Immune Image Sharpness Metric[C], 2005 IEEE International Conference on Image Processing, 2005, 1, 405-408.

DOI: 10.1109/icip.2005.1529773

[10] X.Y. LIU, W.H. WANG, Y. SUN. Dynamic evaluation of autofocusing for automated microscopic analysis of blood smear and pap smear[J], Journal of Microscopy, (2007), 227(1): 15–23.

DOI: 10.1111/j.1365-2818.2007.01779.x

[11] Almir Kimura Junior, Marly G. F. Costa, Cicero F. F. Costa Filho, et al. Evaluation of autofocus functions of conventional sputum smear microscopy for tuberculosis[C], IEEE International Conference on Engineering in Medicine and Biology Society (EMBS), (2010).

DOI: 10.1109/iembs.2010.5626143

[12] Rony Ferzli and Lina J. Karam. Human Visual System Based No-Reference Objective Image Sharpness Metric[C], 2006 IEEE International Conference on Image Processing, (2006), 2949 – 2952.

DOI: 10.1109/icip.2006.312925

[13] Timo Ahonen, Matti Pietikainen. Face Description with Local Binary Patterns: Application to Face Recognition[J], IEEE Transactions On Pattern Analysis And Machine Intelligence, (2006), 28(12): 2037-(2041).

DOI: 10.1109/tpami.2006.244

[14] Timo Ojala, Matti Pietikainen, David Harwood. A Comparative Study of Texture Measures with Classification Based on Features Distributions[J], Pattern Recognition, (1996), 29 (1): 51–59.

DOI: 10.1016/0031-3203(95)00067-4

[15] Javier Ruiz-del-Solar, Julio Quinteros. Illumination compensation and normalization in eigenspace-based face recognition: A Comparative Study of Different Pre-processing Approaches[J], Pattern Recognition Letters, (2008), 29(14): 1966-(1979).

DOI: 10.1016/j.patrec.2008.06.015