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A Morphological Pyramids Approach to Grayscale Image Enhancement

by Anthony Aidoo, Frank Arthur, Gloria A. Botchway
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 31
Year of Publication: 2022
Authors: Anthony Aidoo, Frank Arthur, Gloria A. Botchway
10.5120/ijca2022922363

Anthony Aidoo, Frank Arthur, Gloria A. Botchway . A Morphological Pyramids Approach to Grayscale Image Enhancement. International Journal of Computer Applications. 184, 31 ( Oct 2022), 1-10. DOI=10.5120/ijca2022922363

@article{ 10.5120/ijca2022922363,
author = { Anthony Aidoo, Frank Arthur, Gloria A. Botchway },
title = { A Morphological Pyramids Approach to Grayscale Image Enhancement },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2022 },
volume = { 184 },
number = { 31 },
month = { Oct },
year = { 2022 },
issn = { 0975-8887 },
pages = { 1-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number31/32509-2022922363/ },
doi = { 10.5120/ijca2022922363 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:22:51.260317+05:30
%A Anthony Aidoo
%A Frank Arthur
%A Gloria A. Botchway
%T A Morphological Pyramids Approach to Grayscale Image Enhancement
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 31
%P 1-10
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Medical image processing algorithms significantly affect the precision of disease diagnostic process. This makes it crucial to improve the quality of a medical image with the goal to enhance perceivability of the points of interest in order to obtain accurate diagnosis of a patient. Despite the reliance of various medical diagnostics on utilize X-rays, they are usually plagued by dark and low contrast properties. Sought-after details in X-rays can only be accessed by means of digital image processing techniques, despite the fact that these techniques are far from being perfect. In this paper, we implement a wavelet decomposition and reconstruction technique to enhance radiograph properties, some of which include contrast and noise, by using a series of morphological erosion and dilation to improve the visual quality of the chest radiographs for the detection of cancer nodules.

References
  1. H. S. S. Ahmed and M. J. Nordin. Improving diagnostic viewing of medical images using enhancement algorithms. Journal of Computer Science, 7(12):1831, 2011.
  2. H. E. Averette. MD.“Chapter 33-Gynecologic Cancer”. American Cancer Society Textbook of Clinical Oncology, 2nd Edition. GP Murphy, W. Lawrence, RE Lenhard, Eds.(American Cancer Society, Inc, Atlanta, 1995) pp556- 560, 1995.
  3. G. Boato, D.-T. Dang-Nguyen, and F. G. B. De Natale. Morphological filter detector for image forensics applications. Ieee Access, 8:13549–13560, 2020.
  4. M. Boix and B. Cant´o. Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images. Mathematical Biosciences & Engineering, 10(2):279, 2013.
  5. S. Chen, H. Hou, Y. J. Zeng, and X. Xu. Automatic enhancement for chest radiographs. J. of Digital Imaging, 4:371–375, 2006.
  6. S. Chen and K. Suzuki. Separation of bones from chest radiographs by means of anatomically specific multiple massivetraining ANNs combined with total variation minimization smoothing. IEEE transactions on medical imaging, 33(2):246–257, 2014.
  7. L. P. Coelho. Mahotas: Open source software for scriptable computer vision. arXiv preprint arXiv:1211.4907, 2013.
  8. G. Coppini, S. Diciotti, M. Falchini, N. Villari, and G. Valli. Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms. IEEE Transactions on Information Technology in Biomedicine, 7(4):344–357, 2003.
  9. C. Di Rubeto, A. Dempster, S. Khan, and B. Jarra. Segmentation of blood images using morphological operators. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, volume 3, pages 397–400. IEEE, 2000.
  10. G. P. Fenoy. Adaptive wavelets and their applications to image fusion and compression. Univ., 2003.
  11. J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer, 136(5):E359–E386, 2015.
  12. L. Florence, F. Guy, and A. Olivier. The morphological pyramid and its applications to remote sensing: Multiresolution data analysis and features extraction. Image Analysis & Stereology, 21(1):49–53, 2002.
  13. G. Flouzat, O. Amram, F. Laporterie, and S. Cherchali. Multiresolution analysis and reconstruction by a morphological pyramid in the remote sensing of terrestrial surfaces. Signal Processing, 81(10):2171–2185, 2001.
  14. B. A. Fomani and A. Shahbahrami. License plate detection using adaptive morphological closing and local adaptive thresholding. In 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), pages 146– 150. IEEE, 2017.
  15. H. J. A. M. Heijmans, J. Goutsias, et al. Multiresolution signal decomposition schemes. Part 1: Linear and morphological pyramids. In IEEE Transactions on Image Processing. Citeseer, 2000.
  16. J. A. M. Heijmans and J. Goutsias. Mathematical Morphology and its Applications to Image and Signal Processing. Kluwer Academic Publishers, 2000.
  17. B. Y. Kwan and H. K. Kwan. Improved lung nodule visualization on chest radiographs using digital filtering and contrast enhancement. World Acad Sci Eng Technol, 110:590–3, 2011.
  18. C. Lee, R. M. Haralick, and T. Phillips. Image segmentation using the morphological pyramid. In Applications of Artificial Intelligence VII, volume 1095, pages 208–221. SPIE, 1989.
  19. T. Lei, Y. Zhang, Y. Wang, S. Liu, and Z. Guo. A conditionally invariant mathematical morphological framework for color images. Information Sciences, 387:34–52, 2017.
  20. Y.-F. Li, M. Zuo, K. Feng, and Y.-J. Chen. Detection of bearing faults using a novel adaptive morphological update lifting wavelet. Chinese Journal of Mechanical Engineering, 30(6):1305–1313, 2017.
  21. J. A. Palmason, J. A. Benediktsson, J. R. Sveinsson, and J. Chanussot. Classification of hyperspectral data from urban areas using morpholgical preprocessing and independent component analysis. In International Geoscience And Remote Sensing Symposium, volume 1, page 176, 2005.
  22. M. Pesaresi and J. A. Benediktsson. A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE transactions on Geoscience and Remote Sensing, 39(2):309–320, 2001.
  23. R. Rajni and A. Anutam. Image denoising techniques-an overview. International Journal of Computer Applications, 86(16):13–17, 2014.
  24. J. B. T. M. Roerdink. Multiresolution maximum intensity volume rendering by morphological adjunction pyramids. IEEE transactions on image processing, 12(6):653–660, 2003.
  25. J. B. T. M. Roerdink. Morphological pyramids in multiresolution MIP rendering of large volume data: Survey and new results. Journal of Mathematical Imaging and Vision, 22(2):143–157, 2005.
  26. K. A. M. Said, A. B. Jambek, and N. Sulaiman. A study of image processing using morphological opening and closing processes. International Journal of Control Theory and Applications, 9(31):15–21, 2016.
  27. G. N. Sarage and S. Jambhorkar. Enhancement of chest xray images using filtering techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 2(5):308–312, 2012.
  28. A. M. R. Schilham, B. v. Ginneken, and M. Loog. Multi-scale nodule detection in chest radiographs. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 602–609. Springer, 2003.
  29. J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K.-i. Komatsu, M. Matsui, H. Fujita, Y. Kodera, and K. Doi. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. American Journal of Roentgenology, 174(1):71–74, 2000.
  30. A. Suero, D. Marin, M. E. Geg´undez-Arias, and J. M. Bravo. Locating the Optic Disc in Retinal Images Using Morphological Techniques. In IWBBIO, pages 593–600, 2013.
  31. K. Sun, Z. Chen, S. Jiang, and Y. Wang. Morphological multiscale enhancement, fuzzy filter and watershed for vascular tree extraction in angiogram. Journal of medical systems, 35(5):811–824, 2011.
  32. J. Wei, Y. Hagihara, A. Shimizu, and H. Kobatake. Optimal image feature set for detecting lung nodules on chest Xray images. In CARS 2002 computer assisted radiology and surgery, pages 706–711. Springer, 2002.
  33. M. Wilson, A. Y. Aidoo, C. H. Acquah, and P. A. Yirenkyi. Chest radiograph image enhancement: a total variation approach. International Journal of Computer Applications, 163(7):1–6, 2017.
  34. Q. Yang, X. Zhu, J.-K. Fwu, Y. Ye, G. You, and Y. Zhu. MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations. In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, jan 2021.
  35. B.-W. Yoon and W.-J. Song. Image contrast enhancement based on the generalized histogram. Journal of electronic imaging, 16(3):033005, 2017.
  36. B. Zhang. Reconfigurable Morphological Processor for Grayscale Image Processing. Electronics, 10(19):2429, 2021.
Index Terms

Computer Science
Information Sciences

Keywords

Chest radiograph image enhancement mathematical morphology wavelet decomposition Chest radiograph image enhancement mathematical morphology wavelet decomposition