CONTRAST ENHANCEMENT OF SCANNING ELECTRON MICROSCOPY IMAGES USING A NONCOMPLEX MULTIPHASE ALGORITHM
Zaid ALSAYGH
zaidalobaidy1988@gmail.comDepartment of Computer Science, College of Computer Science and Mathematics, University of Mosul, Nineveh (Iraq)
Zohair AL-AMEEN
Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Nineveh, (Iraq)
Abstract
Microscopic technology has recently flourished, allowing unparalleled viewing of microscopic elements invisible to the normal eye. Still, the existence of unavoidable constraints led on many occasions to have low contrast scanning electron microscopic (SEM) images. Thus, a noncomplex multiphase (NM) algorithm is proposed in this study to provide better contrast for various SEM images. The developed algorithm contains the following stages: first, the intensities of the degraded image are modified using a two-step regularization procedure. Next, a gamma-corrected cumulative distribution function of the logarithmic uniform distribution approach is applied for contrast enhancement. Finally, an automated histogram expansion technique is used to redistribute the pixels of the image properly. The NM algorithm is applied to natural-contrast distorted SEM images, as well as its results are compared with six algorithms with different processing notions. To assess the quality of images, three modern metrics are utilized, in that each metric measures the quality based on unique aspects. Extensive appraisals revealed the adequate processing abilities of the NM algorithm, as it can process many images suitably and its performances outperformed many available contrast enhancement algorithms in different aspects.
Keywords:
image enhancement, SEM images, statistical approaches, contrast enhancementReferences
Abdullah-Al-Wadud, M., Kabir, M. H., Dewan, M. A. A., & Chae, O. (2007). A dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(2), 593–600. http://doi.org/10.1109/TCE.2007.381734
Al‐Ameen, Z. (2018a). An improved contrast equalization technique for contrast enhancement in scanning electron microscopy images. Microscopy Research and Technique, 81(10), 1132–1142. https://doi.org/10.1002/jemt.23100
Al-Ameen, Z. (2018b). Expeditious contrast enhancement for grayscale images using a new swift algorithm. Statistics, Optimization & Information Computing, 6(4), 577–587. https://doi.org/10.19139/soic.v6i4.436
Al-Ameen, Z. (2020). Satellite image enhancement using an ameliorated balance contrast enhancement technique. Traitement du Signal, 37(2), 245–254. https://doi.org/10.18280/ts.370210
Arya, V., Sharma, V., & Arya, G. (2019). An efficient adaptive algorithm for electron microscopic image enhancement and feature extraction. International Journal of Computer Vision and Image Processing, 9(1), 1–16. https://doi.org/10.4018/IJCVIP.2019010101
Beekman, P., Enciso-Martinez, A., Rho, H. S., Pujari, S. P., Lenferink, A., Zuilhof, H., Terstappen, L.W. M. M., Otto, C., & Le Gac, S. (2019). Immuno-capture of extracellular vesicles for individual multi-modal characterization using AFM, SEM and Raman spectroscopy. Lab on a Chip, 19(15), 2526–2536. https://doi.org/10.1039/C9LC00081J
Bennet, F., Burr, L., Schmid, D., & Hodoroaba, V. D. (2021). Towards a method for quantitative evaluation of nanoparticle from suspensions via microarray printing and SEM analysis. Journal of Physics: Conference Series, 1953(1), 012002.
Cakir, S., Kahraman, D. C., Cetin-Atalay, R., & Cetin, A. E. (2018). Contrast enhancement of microscopy images using image phase information. IEEE Access, 6, 3839–3850. https://doi.org/10.1109/access.2018.2796646
Celik, T. (2014). Spatial entropy-based global and local image contrast enhancement. IEEE Transactions on Image Processing, 23(12), 5298-5308. https://doi.org/10.1109/TIP.2014.2364537
Chen, J., Yu, W., Tian, J., Chen, L., & Zhou, Z. (2018). Image contrast enhancement using an artificial bee colony algorithm. Swarm and Evolutionary Computation, 38, 287–294. https://doi.org/10.1016/j.swevo.2017.09.002
Chen, S. D., & Ramli, A. R. (2003). Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Transactions on Consumer Electronics, 49(4), 1301–1309.
Cocks, E., Taggart, M., Rind, F. C., & White, K. (2018). A guide to analysis and reconstruction of serial block face scanning electron microscopy data. Journal of Microscopy, 270(2), 217–234. https://doi.org/10.1111/jmi.12676
El Malali, H., Assir, A., Bhateja, V., Mouhsen, A., & Harmouchi, M. (2020). A contrast enhancement model for x-ray mammograms using modified local s-curve transformation based on multi-objective optimization. IEEE Sensors Journal, 21(10), 11543–11554. https://doi.org/10.1109/JSEN.2020.3028273
Feng, H., Ye, J., & Pease, R. F. (2006). Pattern reconstruction of scanning electron microscope images using long-range content complexity analysis of the edge ridge signal. Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena, 24(6), 3110–3114.
Hamming, R. W. (1970). On the distribution of numbers. The Bell System Technical Journal, 49(8), 1609–1625. https://doi.org/10.1002/j.1538-7305.1970.tb04281.x
Hashemi, S., Kiani, S., Noroozi, N., & Moghaddam, M. E. (2010). An image contrast enhancement method based on genetic algorithm. Pattern Recognition Letters, 31(13), 1816–1824.
Jang, I. S., Kyung, W. J., Lee, T. H., & Ha, Y. H. (2011). Local contrast enhancement based on adaptive multiscale retinex using intensity distribution of input image. Journal of Imaging Science and Technology, 55(4), 1–14.
Lal, S., & Chandra, M. (2014). Efficient algorithm for contrast enhancement of natural images. International Arab Journal of Information Technology, 11(1), 95–102.
Lu, C. H., Hsu, H. Y., & Wang, L. (2009, May). A new contrast enhancement technique by adaptively increasing the value of histogram. In 2009 IEEE international workshop on imaging systems and techniques (pp. 407–411). IEEE. https://doi.org/10.1109/IST.2009.5071676
Ma, H., & Han, L. (2014). Multi-technology integration based on low-contrast microscopic image enhancement. Sensors & Transducers, 163(1), 96–102.
Mello-Román, J. C., Noguera, J. L. V., Legal-Ayala, H., Pinto-Roa, D. P., Monteiro, M. M., & Colmán, J. C. A. L. (2021). Microscopy mineral image enhancement using multiscale top-hat transform. In 2021 XLVII Latin American Computing Conference (CLEI) (pp. 1–6). IEEE. https://doi.org/10.1109/CLEI53233.2021.9639975
Min, X., Gu, K., Zhai, G., Liu, J., Yang, X., & Chen, C. W. (2017). Blind quality assessment based on pseudoreference image. IEEE Transactions on Multimedia, 20(8), 2049–2062. Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012). No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12), 4695–4708.
Ohta, K., Sadayama, S., Togo, A., Higashi, R., Tanoue, R., & Nakamura, K. I. (2012). Beam deceleration for block-face scanning electron microscopy of embedded biological tissue. Micron, 43(5), 612–620. https://doi.org/10.1016/j.micron.2011.11.001
Parihar, A. S., Verma, O. P., & Khanna, C. (2017). Fuzzy-contextual contrast enhancement. IEEE Transactions on Image Processing, 26(4), 1810–1819. https://doi.org/10.1109/TIP.2017.2665975
Pei, S. C., Zeng, Y. C., & Chang, C. H. (2004). Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis. IEEE Transactions on Image Processing, 13(3), 416–429. https://doi.org/10.1109/TIP.2003.821347
Sengee, N., Sengee, A., & Choi, H. K. (2010). Image contrast enhancement using bi-histogram equalization with neighborhood metrics. IEEE Transactions on Consumer Electronics, 56(4), 2727–2734. https://doi.org/10.1109/TCE.2010.5681162
Shukri, N. M., Sim, K. S., & Leong, J. W. (2016). Minimum mean brightness error quad histogram equalization for scanning electron microscope images. In 2016 International Conference on Robotics, Automation and Sciences (ICORAS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICORAS.2016.7872601
Sim, K. S., Teh, V., Tey, Y. C., & Kho, T. K. (2016). Local dynamic range compensation for scanning electron microscope imaging system by sub‐blocking multiple peak HE with convolution. Scanning, 38(6), 492–501. https://doi.org/10.1002/sca.21285
Sim, K. S., Ting, F. F., Leong, J. W., & Tso, C. P. (2019). Signal-to-noise ratio estimation for SEM single image using cubic spline interpolation with linear least square regression. Engineering Letters, 27(1), 151–165.
Sutton, M. A., Li, N., Joy, D. C., Reynolds, A. P., & Li, X. (2007). Scanning electron microscopy for quantitative small and large deformation measurements part I: SEM imaging at magnifications from 200 to 10,000. Experimental Mechanics, 47(6), 775–787. https://doi.org/10.1007/s11340-007-9042-z
Vladár, A. E., Postek, M. T., & Ming, B. (2009). On the sub-nanometer resolution of scanning electron and helium ion microscopes. Microscopy Today, 17(2), 6–13. https://doi.org/10.1017/S1551929500054420
Wighting, M. J., Lucking, R. A., & Christmann, E. P. (2004). The latest in handheld microscopes. Science Scope, 6, 58–61.
Al‐Ameen, Z. (2018a). An improved contrast equalization technique for contrast enhancement in scanning electron microscopy images. Microscopy Research and Technique, 81(10), 1132–1142. https://doi.org/10.1002/jemt.23100
Al-Ameen, Z. (2018b). Expeditious contrast enhancement for grayscale images using a new swift algorithm. Statistics, Optimization & Information Computing, 6(4), 577–587. https://doi.org/10.19139/soic.v6i4.436
Al-Ameen, Z. (2020). Satellite image enhancement using an ameliorated balance contrast enhancement technique. Traitement du Signal, 37(2), 245–254. https://doi.org/10.18280/ts.370210
Arya, V., Sharma, V., & Arya, G. (2019). An efficient adaptive algorithm for electron microscopic image enhancement and feature extraction. International Journal of Computer Vision and Image Processing, 9(1), 1–16. https://doi.org/10.4018/IJCVIP.2019010101
Beekman, P., Enciso-Martinez, A., Rho, H. S., Pujari, S. P., Lenferink, A., Zuilhof, H., Terstappen, L.W. M. M., Otto, C., & Le Gac, S. (2019). Immuno-capture of extracellular vesicles for individual multi-modal characterization using AFM, SEM and Raman spectroscopy. Lab on a Chip, 19(15), 2526–2536. https://doi.org/10.1039/C9LC00081J
Bennet, F., Burr, L., Schmid, D., & Hodoroaba, V. D. (2021). Towards a method for quantitative evaluation of nanoparticle from suspensions via microarray printing and SEM analysis. Journal of Physics: Conference Series, 1953(1), 012002.
Cakir, S., Kahraman, D. C., Cetin-Atalay, R., & Cetin, A. E. (2018). Contrast enhancement of microscopy images using image phase information. IEEE Access, 6, 3839–3850. https://doi.org/10.1109/access.2018.2796646
Celik, T. (2014). Spatial entropy-based global and local image contrast enhancement. IEEE Transactions on Image Processing, 23(12), 5298-5308. https://doi.org/10.1109/TIP.2014.2364537
Chen, J., Yu, W., Tian, J., Chen, L., & Zhou, Z. (2018). Image contrast enhancement using an artificial bee colony algorithm. Swarm and Evolutionary Computation, 38, 287–294. https://doi.org/10.1016/j.swevo.2017.09.002
Chen, S. D., & Ramli, A. R. (2003). Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Transactions on Consumer Electronics, 49(4), 1301–1309.
Cocks, E., Taggart, M., Rind, F. C., & White, K. (2018). A guide to analysis and reconstruction of serial block face scanning electron microscopy data. Journal of Microscopy, 270(2), 217–234. https://doi.org/10.1111/jmi.12676
El Malali, H., Assir, A., Bhateja, V., Mouhsen, A., & Harmouchi, M. (2020). A contrast enhancement model for x-ray mammograms using modified local s-curve transformation based on multi-objective optimization. IEEE Sensors Journal, 21(10), 11543–11554. https://doi.org/10.1109/JSEN.2020.3028273
Feng, H., Ye, J., & Pease, R. F. (2006). Pattern reconstruction of scanning electron microscope images using long-range content complexity analysis of the edge ridge signal. Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena, 24(6), 3110–3114.
Hamming, R. W. (1970). On the distribution of numbers. The Bell System Technical Journal, 49(8), 1609–1625. https://doi.org/10.1002/j.1538-7305.1970.tb04281.x
Hashemi, S., Kiani, S., Noroozi, N., & Moghaddam, M. E. (2010). An image contrast enhancement method based on genetic algorithm. Pattern Recognition Letters, 31(13), 1816–1824.
Jang, I. S., Kyung, W. J., Lee, T. H., & Ha, Y. H. (2011). Local contrast enhancement based on adaptive multiscale retinex using intensity distribution of input image. Journal of Imaging Science and Technology, 55(4), 1–14.
Lal, S., & Chandra, M. (2014). Efficient algorithm for contrast enhancement of natural images. International Arab Journal of Information Technology, 11(1), 95–102.
Lu, C. H., Hsu, H. Y., & Wang, L. (2009, May). A new contrast enhancement technique by adaptively increasing the value of histogram. In 2009 IEEE international workshop on imaging systems and techniques (pp. 407–411). IEEE. https://doi.org/10.1109/IST.2009.5071676
Ma, H., & Han, L. (2014). Multi-technology integration based on low-contrast microscopic image enhancement. Sensors & Transducers, 163(1), 96–102.
Mello-Román, J. C., Noguera, J. L. V., Legal-Ayala, H., Pinto-Roa, D. P., Monteiro, M. M., & Colmán, J. C. A. L. (2021). Microscopy mineral image enhancement using multiscale top-hat transform. In 2021 XLVII Latin American Computing Conference (CLEI) (pp. 1–6). IEEE. https://doi.org/10.1109/CLEI53233.2021.9639975
Min, X., Gu, K., Zhai, G., Liu, J., Yang, X., & Chen, C. W. (2017). Blind quality assessment based on pseudoreference image. IEEE Transactions on Multimedia, 20(8), 2049–2062. Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012). No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12), 4695–4708.
Ohta, K., Sadayama, S., Togo, A., Higashi, R., Tanoue, R., & Nakamura, K. I. (2012). Beam deceleration for block-face scanning electron microscopy of embedded biological tissue. Micron, 43(5), 612–620. https://doi.org/10.1016/j.micron.2011.11.001
Parihar, A. S., Verma, O. P., & Khanna, C. (2017). Fuzzy-contextual contrast enhancement. IEEE Transactions on Image Processing, 26(4), 1810–1819. https://doi.org/10.1109/TIP.2017.2665975
Pei, S. C., Zeng, Y. C., & Chang, C. H. (2004). Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis. IEEE Transactions on Image Processing, 13(3), 416–429. https://doi.org/10.1109/TIP.2003.821347
Sengee, N., Sengee, A., & Choi, H. K. (2010). Image contrast enhancement using bi-histogram equalization with neighborhood metrics. IEEE Transactions on Consumer Electronics, 56(4), 2727–2734. https://doi.org/10.1109/TCE.2010.5681162
Shukri, N. M., Sim, K. S., & Leong, J. W. (2016). Minimum mean brightness error quad histogram equalization for scanning electron microscope images. In 2016 International Conference on Robotics, Automation and Sciences (ICORAS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICORAS.2016.7872601
Sim, K. S., Teh, V., Tey, Y. C., & Kho, T. K. (2016). Local dynamic range compensation for scanning electron microscope imaging system by sub‐blocking multiple peak HE with convolution. Scanning, 38(6), 492–501. https://doi.org/10.1002/sca.21285
Sim, K. S., Ting, F. F., Leong, J. W., & Tso, C. P. (2019). Signal-to-noise ratio estimation for SEM single image using cubic spline interpolation with linear least square regression. Engineering Letters, 27(1), 151–165.
Sutton, M. A., Li, N., Joy, D. C., Reynolds, A. P., & Li, X. (2007). Scanning electron microscopy for quantitative small and large deformation measurements part I: SEM imaging at magnifications from 200 to 10,000. Experimental Mechanics, 47(6), 775–787. https://doi.org/10.1007/s11340-007-9042-z
Vladár, A. E., Postek, M. T., & Ming, B. (2009). On the sub-nanometer resolution of scanning electron and helium ion microscopes. Microscopy Today, 17(2), 6–13. https://doi.org/10.1017/S1551929500054420
Wighting, M. J., Lucking, R. A., & Christmann, E. P. (2004). The latest in handheld microscopes. Science Scope, 6, 58–61.
ALSAYGH, Z. ., & AL-AMEEN, Z. (2022). CONTRAST ENHANCEMENT OF SCANNING ELECTRON MICROSCOPY IMAGES USING A NONCOMPLEX MULTIPHASE ALGORITHM. Applied Computer Science, 18(2), 28–42. https://doi.org/10.35784/acs-2022-11
Authors
Zaid ALSAYGHzaidalobaidy1988@gmail.com
Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Nineveh Iraq
Authors
Zohair AL-AMEENDepartment of Computer Science, College of Computer Science and Mathematics, University of Mosul, Nineveh, Iraq
Statistics
Abstract views: 132PDF downloads: 73
License
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Marcin TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF IMAGE ANALYSIS TO THE IDENTIFICATION OF MASS INERTIA MOMENTUM IN ELECTROMECHANICAL SYSTEM WITH CHANGEABLE BACKLASH ZONE , Applied Computer Science: Vol. 15 No. 3 (2019)
- Baldemar ZURITA, Luís LUNA, José HERNÁNDEZ, Federico RAMÍREZ, BOVW FOR CLASSIFICATION IN GEOMETRICS SHAPES , Applied Computer Science: Vol. 14 No. 4 (2018)
- Dariusz PLINTA, Martin KRAJČOVIČ, APPLICATION OF THE AUGMENTED REALITY IN PRODUCTION PRACTICE , Applied Computer Science: Vol. 13 No. 2 (2017)
- Lukas BAUER, Leon STÜTZ, Markus KLEY, BLACK BOX EFFICIENCY MODELLING OF AN ELECTRIC DRIVE UNIT UTILIZING METHODS OF MACHINE LEARNING , Applied Computer Science: Vol. 17 No. 4 (2021)
- Nawazish NAVEED, Hayan T. MADHLOOM, Mohd Shahid HUSAIN, BREAST CANCER DIAGNOSIS USING WRAPPER-BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORK , Applied Computer Science: Vol. 17 No. 3 (2021)
- Robert KARPIŃSKI, KNEE JOINT OSTEOARTHRITIS DIAGNOSIS BASED ON SELECTED ACOUSTIC SIGNAL DISCRIMINANTS USING MACHINE LEARNING , Applied Computer Science: Vol. 18 No. 2 (2022)
- Lei Liu, Eric B. Blancaflor, Mideth Abisado, A LIGHTWEIGHT MULTI-PERSON POSE ESTIMATION SCHEME BASED ON JETSON NANO , Applied Computer Science: Vol. 19 No. 1 (2023)
- Archana Gunakala, Afzal Hussain Shahid, A COMPARATIVE STUDY ON PERFORMANCE OF BASIC AND ENSEMBLE CLASSIFIERS WITH VARIOUS DATASETS , Applied Computer Science: Vol. 19 No. 1 (2023)
- Mohanad ABDULHAMID, Otieno ODONDI, Muaayed AL-RAWI, COMPUTER VISION BASED ON RASPBERRY PI SYSTEM , Applied Computer Science: Vol. 16 No. 4 (2020)
You may also start an advanced similarity search for this article.
