Medical Image Enhancement through Dual Tree Complex Wavelet Transform and Multi-resolution Gabor Filter

##plugins.themes.academic_pro.article.main##

Gouri Morankar

Abstract

Medical image processing is essential in the field of clinical medicine for analysis and diagnosis of diseases. Most of these medical images are acquired under low illumination and noisy environment. Hence it produces a bottleneck in the processing of medical images through computer vision system. Due to tremendous details and complexity of the medical images, image enhancement is challenging and difficult task. In this paper, medical image enhancement through dual tree complex wavelet transform (DTCWT) and multi-resolution Gabor filter is proposed. After the medical images are acquired using standard techniques, DTCWT is applied that decomposes the gray scale image into high and low frequency subbands. Further DTCWT is applied twice on low frequency subbands to decompose into its high and low frequency subbands. Then multiresolution Gabor filter is applied to decomposed six high frequency subands and two low frequency subband to obtain enhancement in multiple directions. Finally inverse DTCWT is applied to obtain enhanced medical image. The experimental results demonstrate that the approach based on DTCWT and multi-resolution Gabor filter can produce better image enhancement as compared with the conventional and latest methods. The proposed medical image enhancement technique can be applied in analysis and diagnosis of diseases through computer vision system.

##plugins.themes.academic_pro.article.details##

Author Biography

Gouri Morankar, Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India

Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India. Email: [email protected]

How to Cite
Morankar, G. (2021). Medical Image Enhancement through Dual Tree Complex Wavelet Transform and Multi-resolution Gabor Filter. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.444

References

  1. Bai, X., F. Zhou., and, B. Xue. 2012. Image enhancement using multi scale image features extracted by top-hat transform. Optical Laser Technology. Vol.44, No.2, pp.328–336. DOI: https://doi.org/10.1016/j.optlastec.2011.07.009
  2. J. C. Fu., J. W. Chai., and, S. T. C. Wong. 2000. Wavelet-based enhancement for detection of left ventricular myocardial boundaries in magnetic resonance images. Magnetic Resonance Imaging. Vol.18, No.9, pp.1135–1141. DOI: https://doi.org/10.1016/S0730-725X(00)00202-2
  3. D. J. Jobson., Z. U. Rahman., and, G. A. Woodell. 1997. Properties and performance of a center/surround Retinex. IEEE Transaction on Image Processing. Vol.6, No.3, pp.451–462. DOI: https://doi.org/10.1109/83.557356
  4. D. J. Jobson., Z. U. Rahman., and, G. A. Woodell. 1997. A multiscale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Transaction on Image Processing. Vol.6, No.7, pp.965–976. DOI: https://doi.org/10.1109/83.597272
  5. N. Kingsbury. 1998. The dual-tree complex wavelet transform: A new technique for shift invariance and directional filters. IEEE Signal Processing Conference. Rhodes, Greece.
  6. N. Kingsbury. 2000. Complex wavelets and shift invariance. IET Seminar on Time Scale Time Frequency Analysis and Applications. London UK. DOI: https://doi.org/10.1049/ic:20000554
  7. D. Li., L. Zhang., C. Sun., T. Yin., C. Liu ., and, J. Yang. 2019. Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method. IEEE Access. Vol.7, pp.47303–47316. DOI: https://doi.org/10.1109/ACCESS.2019.2909788
  8. S. G. Mallat. 1989. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transaction on Pattern Analyics Machine Intelligence. Vol.11, No.7, pp.674–693. DOI: https://doi.org/10.1109/34.192463
  9. Z. Rahman., D. J. Jobson., and, G. A. Woodell. 1996. Multi-scale retinex for color image enhancement. International Conference on Image Processing. Lausanne, Switzerland.
  10. C. Sazak., C. Nelson., and, B. Obara. 2019. The multiscale bowler-hat transform for blood vessel enhancement in retinal images. Pattern Recognition. Pattern Recognition. Vol.88, pp.739–750. DOI: https://doi.org/10.1016/j.patcog.2018.10.011
  11. C. E. Shannon. 1948. A mathematical theory of communication. Bell System Technical Journal. Vol.27, No.3, pp.379–423. DOI: https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
  12. Stella Vetova., and, Ivan Ivanov. 2014. Image Features Extraction Using The Dual-Tree Complex Wavelet Transform. Advances in Applied and Pure Mathematics. pp.277–282. DOI: https://doi.org/10.1109/MCSI.2014.51
  13. M. Abdullah-Al-Wadud., M. H. Kabir., M. A. A. Dewan., and, O. Chae. 2007. A dynamic histogram equalization for image contrast enhancement. IEEE Transaction on Consumer Electronics. Vol.53, No.2, pp.593–600. DOI: https://doi.org/10.1109/TCE.2007.381734
  14. https://figshare.com/articles/brain tumor dataset/1512427/5.