Abstract
The Alzheimer’s disease (AD) is a neurological disorder. It is a slow ongoing brain disease that starts well before clinical symptoms emerge. The subsequent decline in cognitive functions worsens with time and eventually can lead to death. In this study, we worked on T2 sequence Magnetic Resonance Images (MRI) of the Brain AD affected images. The brain may be affected by the AD in different regions. Proposed work concentrates on the hippocampus region affected by the AD using MRI imaging modality. In order to analysis the medical images with no noise the images are enhanced by using different filtering techniques. The work carried out in this paper discuss about different filtering techniques like Gaussian, Median, Wiener and order statistical filter. The performance of these algorithm were examined on PSNR and RMSE values. After the computation the median filter is consider to be the good filter as per the results for the MRI images of AD.
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
Alzheimer’s disease facts, figures. Alzheimer’s and dementia. J. Alzheimer’s Assoc. 13(4), 325–373 (2017). https://doi.org/10.1016/j.jalz.2017.02.001
Amaral, D., Lavenex, P.: Hippocampus neuroanatomy. In: Anderson, P., Morris, R., Amaral, D., Bliss, T., O’Keefe, J. (eds.) The Hippocampus Book, 1st edn, p. 37. Oxford University Press, New York (2007). ISBN 978-0-19-510027-3
Alzheimer’s Disease Neuroimaging Initiative. ADNI for Alzheimer’s disease database. http://adni.loni.usc.edu
Santosh, K.C., Wendling, L., Antani, S., Thoma, G.R.: Overlaid arrow detection for labeling regions of interest in biomedical images. IEEE Intell. Syst. 31(3), 66–75 (2016). Browse Journal and Magazines
Santosh, K.C., Alam, N., Roy, P.P., Wendling, L., Antani, S., Thoma, G.R.: A simple and efficient arrowhead detection technique in biomedical images. Int. J. Pattern Recogn. Artif. Intell. 30(05), 1657002 (2016)
Santosh, K.C., Roy, P.P.: Arrow detection in biomedical images using sequential classifier. Int. J. Mach. Learn. Cybern. 9(6), 993–1006 (2018)
Kumar, N., Nachamai, M.: Noise removal and filtering techniques used in medical images. Orient. J. Comput. Sci. Technol. 10(1), 103–113 (2017). ISSN 0974-6471
Ali, H.M.: MRI Medical Image Denoising by Fundamental
Temizel, A., Vlachos, T.: Wavelet domain image resolution enhancement using cycle-spinning. Electron. Lett. 41(3), 119–121 (2005)
Temizel, A., Vlachos, T.: Image resolution upscaling in the wavelet domain using directional cycle spinning. J. Electron. Imaging 14(4), 040501 (2005)
Anbarjafari, G., Demirel, H.: Image super resolution based on interpolation of wavelet domain high frequency subbands and the spatial domain input image. ETRI J 32(3), 390–394 (2010)
Chaurasia, K., Sharma, N.: Performance evaluation and comparison of different noise, apply on PNG image format used in deconvolution Wiener filter (FFT) algorithm. Evol. Trends Eng. Technol. 4, 8–14 (2015). https://doi.org/10.18052/www.scipress.com/ETET.4.8. ISSN 2349-915X
Gonzalez, R.C., Woods, R.E.: Digital Image Processing Using MATLAB, 3rd edn. Pearson Education, London (2003)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision, 2nd edn. Thomson Learning, Boston (2001)
Makandar, A., Somshekhar, R.: Approaches of pattern recognition in analysing the neurological disorders: a review. Int. J. Trend Sci. Res. Dev. (IJTSRD) Int. Open Access J. 2(3), 1729–1733 (2018). http://www.ijtsrd.com. ISSN 2456-6470
Yalabık, N.: Medical applications of pattern recognition. In: HIBIT 2010, Antalya, April 2010
Makandar, A., Halalli, B.: Threshold based segmentation technique for mass detection in mammography. J. Comput. 11(6), 472–478 (2016)
Makandar, A., Patrot, A.: Malware image analysis and classification using support vector machine. Int. J. Trends Comput. Sci. Eng. 4(5), 01–03 (2015)
Makandar, A.U.R., Karibasappa, K.: Wavelet based medical image compression using SPHIT. J. Comput. Sci. Math. Sci. 1, 769–775 (2010)
Alzhimer’s Association. https://www.alz.org/alzheimers_diseaseAlzheimersAssociation
Hippocampus Wikipedia. https://en.wikipedia.org/wiki/Hippocampus
Anderson, P., Morris, R., Amaral, D., Bliss, T., O’Keefe, J. (eds.): The Hippocampus Book. Oxford University Press, Oxford (2007). ISBN 978-0-19-510027-3
Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Automated fractured bone segmentation and labeling from CT images. J. Med. Syst. (2019). https://doi.org/10.1007/s10916-019-1176-x
Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Segmentation and analysis of CT images for bone fracture detection and labeling. In: Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques, chap. 7. CRC Press, Boca Raton (2019). ISBN 9780367139612
Hegadi, R.S., Navale, D.I., Pawar, T.D., Ruikar, D.D.: Multi feature-based classification of osteoarthritis in knee joint X-ray images. In: Medical imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques chap. 5. CRC Press, Boca Raton (2019). ISBN 9780367139612
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Makandar, A., Somshekhar, R. (2019). Image Enhancement Using Filters on Alzheimer’s Disease. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_3
Download citation
DOI: https://doi.org/10.1007/978-981-13-9184-2_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9183-5
Online ISBN: 978-981-13-9184-2
eBook Packages: Computer ScienceComputer Science (R0)