An Image Segmentation for Different Medical Image Modalities using Wavelet Transform Technique

Authors

  • Rinisha Bagaria  PhD Research Scholar, Electrical Engineering Department, MITS Gwalior, Madhya Pradesh, India
  • Sulochana Wadhwani  Head & Professor, Electrical Engineering Department, MITS Gwalior, Madhya Pradesh, India
  • A. K. Wadhwani  Professor, Electrical Engineering Department, MITS Gwalior, Madhya Pradesh, India

DOI:

https://doi.org/10.32628/IJSRST2310134

Keywords:

Image Segmentation, Wavelet Transform, Image Quality Evaluation, Medical Image Modalities.

Abstract

Image Segmentation techniques are emerging every day in the medical environment. To retain the details of the medical images and obtain the suitable image segmentation method for considered medical image modalities, an image segmentation system based on the Wavelet Transform to decompose at an appropriate level is proposed in this paper. The detailed information of horizontal, vertical and diagonal direction was obtained with the decomposition of images based on the wavelet transform technique. The suggested technique has obvious benefits among the four image quality indexes: sensitivity to noise ratio, peak sensitivity to noise ratio, structure similarity, and entropy, especially for X-ray image segmentation. The method will provide a new ground for further scope in this research work to better understand the image segmentation techniques for medical images.

References

  1. R.Yogamangalam, B.Karthikeyan, “Segmentation Techniques Comparison in Image Processing”, International Journal of Engineering and Technology (IJET), Volume-5, No-1, ISSN: 0975-4024, Feb – Mar 2013.
  2. M. Khan, Ravi. S, “Image Segmentation Methods: A Comparative Study”, International Journal of Soft Computing and Engineering (IJSCE), Volume-3, Issue-4, ISSN: 2231-2307, September 2013.
  3. S. L. S. Abdullaha, Hamirul’Aini Hambalia,b, Nursuriati Jamilc, “Segmentation of Natural Images Using an Improved Thresholding-based Technique”, International Symposium on Robotics and Intelligent Sensors (IRIS), 938 – 944, 2012.
  4. M. S. Pandey, S. S. Soam, S. P. Tripathi, “Recognition of X-rays bones: Challenges in the Past, Present and Future”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-8, Issue-10, ISSN: 2278-3075, August 2019.
  5. N. Umadevi, Dr. S.N Geethalakshmi, “Enhanced Segmentation Method for Bone Structure and Diaphysis Extraction from X-ray Images”, International Journal of Computer Applications Volume-37, No-3, 0975 – 8887, January 2012.
  6. K. O. Wong, S. L. Manske, “A Comparison of Peripheral Imaging Technologies for Bone and Muscle Quantification: A Review of Segmentation Techniques”, Journal of Clinical Densitometry: Assessment & Management of Musculoskeletal Health, Volume-23, No-1, 2020.
  7. J. Dograa, S. Jaina , M. Sooda, “Segmentation of MR Images using Hybrid kMean-Graph Cut Technique”, International Conference on Computational Intelligence and Data Science (ICCIDS), pp 775–784, 2018.
  8. P. Parida, N. Bhoi, “Wavelet based transition region extraction for image segmentation”, Future Computing and Informatics Journal, 2 October 2017.
  9. R. Bagaria, S. Wadhwani, A. K. Wadhwani, “Automatic Location of Fracture Region in a Bone X-Ray Using MATLAB”, IEEE National Conference On Advance Trends And Techniques In Wireless Communication And Soft Computing, Feb 2019.
  10. R. Bagaria, S. Wadhwani, A. K. Wadhwani, “Different Techniques for Identification of a Bone Fracture in Analysis of Medical Image”, 9th IEEE International Conference on Communication Systems and Network Technologies (CSNT), 978-1-7281-4976-9, April 2020.
  11. J. Gao, B. Wang, Z. Wang, Y. Wang, F. Kong, “A wavelet transform-based image segmentation method”, Optik - International Journal for Light and Electron Optics, December 2019.
  12. Gavlasova, A. Prochazka, and M. Mudrova, “Wavelet Based Image Segmentation”, January 2006.
  13. Z. Xizhi, “The Application of Wavelet Transform in Digital Image Processing”, 2008 International Conference on MultiMedia and Information Technology, IEEE, 978-0-7695-3556-2, 2008.
  14. L. Messaouda, R. Abdelghani, Z. Messal, S. Larbi, “Medical Image Segmentation Based on Wavelet Transformation and Level set method”, The 4th International Conference on Power Electronics and their Applications, IEEE, 978-1-7281-2726-2, 25-27 September 2019.
  15. W. K. Ngui, M. S. Leong, L. M. Hee and A. M. Abdelrhman, “Wavelet Analysis: Mother Wavelet Selection Methods”, Applied Mechanics and Materials, Volume- 393, pp 953-958, September 2013.
  16. M. T. Wanjari, Dr. M. P. Dhore, “Document Image Segmentation Using Wavelet Transform and Gabor Filter Technique”, IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN: 2278-0661, ISSN: 2278-8727, pp 25-29, 2016.
  17. W. A. Fourati and M. S. Bouhlel, “Trabecular Bone Image Segmentation Using Wavelet and Marker-Controlled Watershed Transformation”, Hindawi Publishing Corporation Chinese Journal of Engineering, Article ID 891950, 10 pages, June 2014.
  18. D. Tsai, Y. Lee, “A Method Of Medical Image Enhancement Using Wavelet-Coefficient Mapping Functions”, IEEE Conf. Neural Networks & Signal Processing, Nanjing, China, December 14-17, 2003.
  19. R. Bagaria, S. Wadhwani, A. K. Wadhwani "A Wavelet Based Segmentation Technique For Medical Images", in 2nd International Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering & Technology (ICSISCET - 2020), Gwalior, India, 2020, pp. 2329-7182.
  20. K. Dimililer, “IBFDS: Intelligent bone fracture detection system”, 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW 2017, pp 260-267, August 2017.
  21. Kaur, L. Kaur and S. Gupta, “Image Recognition using Coefficient of Correlation and Structural SIMilarity Index in Uncontrolled Environment”, International Journal of Computer Applications, Volume-59, No-5, 0975 – 8887, December 2012.
  22. Z. M. Ramadan, “Using Entropy and 2-D Correlation Coefficient as Measuring Indices for Impulsive Noise Reduction Techniques”, International Journal of Applied Engineering Research, Volume-12, N0-21, ISSN 0973-4562, pp. 11101-11106, 2017.
  23. J. Li, W. Dai, “Image Quality Assessment Based on the Correlation coefficient and the 2-D Discrete Wavelet Transform”, Proceedings of the IEEE International Conference on Automation and Logistics Shenyang, China, IEEE, 978-1-4244-4795-4, August 2009.

International Journal of Scientific Research in Science and Technology

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Published

2023-02-28

Issue

Volume 10, Issue 1, January-February-2023

Section

Research Articles

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How to Cite

[1]
Rinisha Bagaria, Sulochana Wadhwani, A. K. Wadhwani "An Image Segmentation for Different Medical Image Modalities using Wavelet Transform Technique" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 1, pp.292-307, January-February-2023. Available at doi : https://doi.org/10.32628/IJSRST2310134