Abstract
In this paper we present a generalized algorithm for unsharp masking of medical images which takes as one of its inputs a high contrast image underwent local adaptive contrast enhancement. Selection of optimal values of the number of histogram bins, processing window size and intensity lower and upper limits in iterative manner is part of applying Contrast Limited Adaptive Histogram Equalization (CLAHE). Experimental results reveal higher quality of the output images both in terms of root mean square contrast and sharpness. Achieved quality, both visually and quantitatively, is compared to that from the Adaptive Histogram Equalization (AHE) algorithm, limited histogram stretching and ordinary histogram equalization which proves its applicability. The algorithm is considered appropriate for processing a number of types of images, such as CT, X-ray, etc.
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
References
Gonzalez, R., Woods, R.: Digital Image Processing, 4th edn. Pearson, New York (2018)
Burger, W., Burge, M.J.: Principles of Digital Image Processing: Core Algorithms. Springer Science & Business Media, London (2010)
Beutel, J., Kundel, H.L., Van Metter, R.L.: Handbook of medical imaging, vol. 1. Spie Press, Washington (2000)
Philbrick, K.A., et al.: What does deep learning see? Insights from a classifier trained to predict contrast enhancement phase from CT images. Am. J. Roentgenol. 211(6), 1184–1193 (2018)
Kallel, F., Hamida, A.B.: A new adaptive gamma correction based algorithm using DWT-SVD for non-contrast CT image enhancement. IEEE Trans. Nanobiosci. 16(8), 666–675 (2017)
Mehmood, A., Khan, I.R., Dawood, H., Dawood, H.: Enhancement of CT images for visualization. In: ACM SIGGRAPH 2019 Posters, pp. 1–2 (2019)
Irrera, P., Bloch, I., Delplanque, M.: A flexible patch based approach for combined denoising and contrast enhancement of digital X-ray images. Med. Image Anal. 28, 33–45 (2016)
Kushol, R., Raihan, M., Salekin, M.S., Rahman, A.B.M.: Contrast enhancement of medical x-ray image using morphological operators with optimal structuring element. arXiv preprint arXiv:1905.08545 (2019)
Qui, J., Li, H.H., Zhang, T., Ma, F., Yang, D.: Automatic x-ray image contrast enhancement based on parameter auto-optimization. J. Appl. Clin. Med. Phys. 18(6), 218–223 (2017)
Blinchikoff, H.J., Zverev, A.I.: Filtering in the Time and Frequency Domains. Wiley, New York (1976)
Pizer, S.M., et al.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)
Peli, E.: Contrast in complex images. J. Opt. Soc. Am. A 7(10), 2032–2040 (1990)
Präkel, D.: The Visual Dictionary of Photography. AVA Publishing, Lausanne (2010)
Yan, K., Wang, X., Lu, L., Summers, R.M.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 036501 (2018)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097–2106. IEEE, Honolulu, HI, USA, 21–26 July 2017
Acknowledgement
This work was supported by the National Science Fund at the Ministry of Education and Science, Republic of Bulgaria, within the project KP-06-PN-37/55 “Innovative integrated platform for smart management and big data flow analysis for biomedical research”.
Author information
Authors and Affiliations
Corresponding author
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
Draganov, I., Gancheva, V. (2022). Unsharp Masking with Local Adaptive Contrast Enhancement of Medical Images. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_37
Download citation
DOI: https://doi.org/10.1007/978-981-16-3880-0_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3879-4
Online ISBN: 978-981-16-3880-0
eBook Packages: EngineeringEngineering (R0)