Abstract
Microscopy images are acquired from the microscopic view of blood sample analyzed under a microscope. The visual quality of these images is not promising due to its acquisition via lens of the microscope. Guided image filter (GIF) has been suitable for noise suppression; morphological filter (MF) provides a dependable result for contrast enhancement, and unsharp mask (UM) filter is deployed for image sharpening. This paper proposes a combinative fusion approach of aforesaid filter responses using stationary wavelet transform (SWT). The image quality assessment (IQA) of the fused image is evaluated using parameters like peak signal-to-noise ratio (PSNR), enhancement measure estimation (EME), and entropy to adjudge the quality traits of different filter responses. Incremental values of these quality parameters demonstrate that the resultant microscopic images are free from background noises, possesses better contrast and sharpness so that the bacterial clusters are properly differentiated from the background.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zielinski, B., Oleksiak, A.S., Rymarczyk, D., Piekarczyk, A.: Deep learning approach to describing and classifying fungi microscopic images. J. arXiv 1–21 (2020)
Nizar, A., Yigit, A., Isik, Z., Alpkocak, A.: Identification of Leukemia subtypes from microscopic images using convolutional neural network. Diagnostics 9(3), 1–11 (2019)
Jobson, D.J., Rahman, Z., Woodell, G.A.: Spatial aspect of color and scientific implications of retinex image processing. In: Visual Information Processing International Society for Optics and Photonics, vol. 4388, pp. 117–128, Aug 2001
Rahman, Z.U., Jobson, D., Woodell, J., Woodell, G.A.: Retinex processing for automatic image enhancement. J. Electron. Imaging 13(1), 100–110 (2004)
Barnard, K., Funt, B.: Analysis and improvement of multi-scale retinex. In: Color and Imaging, Society for Imaging Science and Technology, vol. 1997, no. 1, pp. 221–226 Jan 1997
He, K., Sun, J., Tang, X.: Guided image filtering. In: Proceeding of European Conference on Computer Vision, pp. 1–14. Berlin, Heidelberg, Sept 2010
Oh, J., Hwang, H.: Feature enhancement of medical images using morphology-based homomorphic filter and differential evolution algorithm. Int. J. Control Autom. Syst. 8(4), 857–861 (2010)
Tek, F.B., Dempster, A.G., Kale, I.: Parasite detection and identification for automated thin blood film malaria diagnosis. Comput. Vis. Image Underst. 114(1), 21–32 (2010)
Deng, G.: A generalized unsharp masking algorithm. IEEE Trans. Image Process. 20(5), 1249–1261 (2010)
Awasthi, N., Katare, P., Gorthi, S.S., Yalavarthy, P.K.: Guided filter based image enhancement for focal error compensation in low cost automated histopathology microscopic system. J. Biophotonics 13(11), 1–23 (2020)
Sharma, A., Bhateja, V., Sinha, A.K.: Synthesis of flash and no-flash image pairs using guided image filtering. In: Proceeding of 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 768–773. Noida, India, Apr 2015
Alankrita, A.R., Shrivastava, A., Bhateja, V.: Contrast improvement of cerebral MRI features using combination of non-linear enhancement operator and morphological filter. In: Proceeding of IEEE International Conference on Network and Computational Intelligence (ICNCI), pp. 182–187. Zhengzhou, China, May 2011
Somasundaram, K., Kalaiselvi, T.: Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations. Comput. Biol. Med. 41(8), 716–725 (2011)
Benson, C.C., Lajish, V.L.: Morphology based enhancement and skull stripping of MRI brain images. In: Proceeding of IEEE International Conference on Intelligent Computing Applications (ICICA), pp. 254—257. Coimbatore, India, Mar 2014
Tiwari, D.K., Bhateja, V., Anand, D., Srivastava, A., Omar, Z.: Combination of EEMD and morphological filtering for baseline wander correction in EMG signals. In: Proceeding of 2nd International Conference on Micro-Electronics, Electromagnetics and Telecommunications, pp. 365–373. Singapore, Sept 2018
Tyagi, T., Gupta, P., Singh, P.: A hybrid multi-focus image fusion technique using SWT and PCA. In: Proceeding of 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 491–497. Noida, India, Jan 2020
Arya, A., Bhateja, V., Nigam, M., Bhadauria, A.S.: Enhancement of brain MR-T1/T2 images using mathematical morphology. In: Proceeding of Information and Communication Technology for Sustainable Development, pp. 833–840. Springer, Singapore, June 2019
The bacterial image dataset (DIBaS) is available online at: http://misztal.edu.pl/software/databases/dibas/. Last Visited on 10 Dec 2020
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, D., Bhateja, V., Yadav, A. (2022). Stationary Wavelet-Based Fusion Approach for Enhancement of Microscopy Images. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_33
Download citation
DOI: https://doi.org/10.1007/978-981-16-6624-7_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6623-0
Online ISBN: 978-981-16-6624-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)