A Novel Pre-Processing Approach for the Denoising of Alzheimer Disease Image Dataset

Authors

  • M. Natarajan Assistant Professor, Division of Computer and Information Science, Annamalai University, Annamalai Nagar, Tamil Nadu, India
  • S. Sathiamoorthy Assistant Professor, Division of Computer and Information Science, Annamalai University, Annamalai Nagar, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajcst-2018.7.2.1870

Keywords:

Thresholding, Histogram Equalization, Wavelet transform, Denoising

Abstract

Medical imaging doing an indispensable part in the area of medicine. Noise in the image is maddening as it worsens the quality of image. Thus, removal of noise is perpetually a problematic work in the images of all domain. Alzheimer’s disease, a neurological dysfunction in which destruction of cells in brain creates mental weakening and memory loss. The distinguished reason for Alzheimer’s disease is low brain activity and low blood flow. We proposed a framework for removal of noise in Alzheimer disease image using histogram equalization, thresholding, open morphological operation and a wavelet transform. This framework reduces the noise and significantly better than the existing methods used for Alzheimer disease images.

References

Yang, Yuan-Han, et al., “Gender’s Effects to the Early Symptoms of Alzheimer’s Disease in 5 Asian Countries”, American Journal of Alzheimer’s Disease & Other Dementias, Vol. 32, No. 4, pp. 194-199, 2017.

Gulhare, Kajal Kiran, S. P. Shukla and L. K. Sharma, “Overview on segmentation and classification for the Alzheimer‟ s disease detection from brain MRI”, pp. 130-132, 2017.

Klosowski, Jakob and Jens Frahm, “Image denoising for real‐time MRI”, Magnetic resonance in medicine, Vol. 77, No. 3, 1340- 1352, 2017.

Khatami, Amin, et al., “Medical image analysis using wavelet transform and deep belief networks”, Expert Systems with Applications, Vol. 86, pp. 190-198, 2017.

Gunja, Ateka, et al., “Image noise reduction technology reduces radiation in a radial-first cardiac catheterization laboratory”, Cardiovascular Revascularization Medicine, Vol. 18, No. 3, pp.197-201, 2017.

Bhadouria, Vivek Singh, et al., “A novel image impulse noise removal algorithm optimized for hardware accelerators”, Journal of Signal Processing Systems, Vol. 89, No. 2, pp. 225-242, 2017.

J. Anitha, J. Dinesh Peter and S. Immanuel Alex Pandian, “A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms”, Computer methods and programs in biomedicine, Vol. 138, pp. 93-104, 2017.

Zear, Aditi, Amit Kumar Singh, and Pardeep Kumar, “A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine”, Multimedia Tools and Applications, Vol. 77, No. 4, pp. 4863-4882, 2018.

Vinay Singh and Deepa Aswani, “Face Detection in Hybrid Color Space Using HBF-KNN”, Proceedings of International Conference on Recent Advancement on Computer and Communication. Springer, Singapore, 2018.

Khwairakpam Amitab, et al., “Impulse Noise Reduction in Digital Images Using Fuzzy Logic and Artificial Neural Network”, Proceedings of the International Conference on Computing and Communication Systems. Springer, Singapore, 2018.

Sayantan Gupta and Sukanya Roy, “Medav Filter—Filter for Removal of Image Noise with the Combination of Median and Average Filters”, Recent Trends in Signal and Image Processing. Springer, Singapore, pp. 11-19, 2019.

Ying Liang and Lei Wang, “Alzheimer’s disease is an important risk factor of fractures: a meta-analysis of cohort studies”, Molecular neurobiology, Vol. 54, No.5, pp. 3230-3235, 2017.

Deepshikha Bhardwaj, et al., “Alzheimer’s disease—Current Status and Future Directions”, Journal of medicinal food, Vol. 20, No.12, pp. 1141-1151, 2017.

Hao He, et al., “Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders”, Brain Structure and Function, Vol. 222, No. 9, pp. 4051-4064, 2017.

Downloads

Published

22-04-2021

How to Cite

Natarajan, M., & Sathiamoorthy, S. (2021). A Novel Pre-Processing Approach for the Denoising of Alzheimer Disease Image Dataset. Asian Journal of Computer Science and Technology, 7(2), 107–112. https://doi.org/10.51983/ajcst-2018.7.2.1870