Identification of Alzheimer Disease: A Literature Survey
DOI:
https://doi.org/10.51983/ajes-2018.7.2.2162Keywords:
Alzheimer Disease, Image Processing, Pre-ProcessingAbstract
Medical imaging works an essential part in the area of medical science. In today scenario, image segmentation is utilized to extricate abnormal tissues from normal tissues directly in medical images. Noise in an image is unacceptable to us as it interrupts and deteriorates the condition of the image. Noise removal is perpetually a challenging responsibility so as of edge protection when the strength of the disturbing noise in the initial image is enormous. Alzheimer's disease is a neurological dysfunction in which the brain death causes cognitive decline and Memory Loss. A neurodegenerative kind of dementia, the condition begins with mild and grows increasingly severe. A crucial area of medical research is Brain image examination, ends to identify brain diseases. The leading causes of Alzheimer’s diseases are Moderate blood flow and brain activity. In this paper, a framework has introduced for the exposure of Alzheimer disease and a literature survey on Image Processing for the AD. This framework optimally determines the Alzheimer field in the neurological disorder.
References
Y. Yang 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.
D. Zhang et al., "Multimodal classification of Alzheimer's disease and mild cognitive impairment," Neuroimage, vol. 55, no. 3, pp. 856-867, 2011.
D. I. Sitzer, E. W. Twamley, and D. V. Jeste, "Cognitive training in Alzheimer's disease: a meta‐analysis of the literature," Acta Psychiatrica Scandinavica, vol. 114, no. 2, pp. 75-90, 2006.
B. Duthey, "Background paper 6.11: Alzheimer disease and other dementias," in A Public Health Approach to Innovation, pp. 1-74, 2013.
W. Thies and L. Bleiler, "Alzheimer’s Association. 2011 Alzheimer’s disease facts and figures," Alzheimer's Dement., vol. 7, pp. 208-44, 2011.
Y. Liang and L. 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.
A. Bhardwaj et al., "Alzheimer's disease-Current Status and Future Directions," Journal of medicinal food, vol. 20, no. 12, pp. 1141-1151, 2017.
H. 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.
J. Klosowski and J. Frahm, "Image denoising for real‐time MRI," Magnetic resonance in medicine, vol. 77, no. 3, pp. 1340-1352, 2017.
A. Khatami et al., "Medical image analysis using wavelet transform and deep belief networks," Expert Systems with Applications, vol. 86, pp. 190-198, 2017.
A. Gunja 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.
V. S. Bhadouria 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. D. Peter, and S. I. A. 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.
A. Zear, A. K. Singh, and P. 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.
V. Singh and D. Aswani, "Face Detection in Hybrid Color Space Using HBF-KNN," in Proceedings of International Conference on Recent Advancement on Computer and Communication. Springer, Singapore, 2018.
K. Amitab et al., "Impulse Noise Reduction in Digital Images Using Fuzzy Logic and Artificial Neural Network," in Proceedings of the International Conference on Computing and Communication Systems. Springer, Singapore, 2018.
S. Gupta and S. Roy, "Medav Filter—Filter for Removal of Image Noise with the Combination of Median and Average Filters," in Recent Trends in Signal and Image Processing. Springer, Singapore, pp. 11-19, 2019.
A. Ortiz et al., "Automatic ROI selection in structural brain MRI using SOM 3D projection," PloS one, vol. 9, no. 4, 2014.
F. Segovia et al., "A comparative study of feature extraction methods for the diagnosis of Alzheimer's disease using the ADNI database," Neurocomputing, vol. 75, no. 1, pp. 64-71, 2012.
J. Ramírez et al., "Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification," Neuroscience letters, vol. 472, no. 2, pp. 99-103, 2010.
P. Padilla et al., "Analysis of SPECT brain images for the diagnosis of Alzheimer's disease based on NMF for feature extraction," Neuroscience letters, vol. 479, no. 3, pp. 192-196, 2010.
W. D. Penny et al., eds., "Statistical parametric mapping: the analysis of functional brain images," Elsevier, 2011.
A. P. Freeborough and N. C. Fox, "MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease," IEEE Transactions on Medical Imaging, vol. 17, no. 3, pp. 475-478, 1998.
Y. Cui et al., "Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors," PloS one, vol. 6, no. 7, e21896, 2011.
I. A. Illán et al., "18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis," Information Sciences, vol. 181, no. 4, pp. 903-916, 2011.
A. Hyvarinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE transactions on Neural Networks, vol. 10, no. 3, pp. 626-634, 1999.
K. Kalti and M. A. Mahjoub, "Image segmentation by gaussian mixture models and modified FCM algorithm," Int. Arab J. Inf. Technol., vol. 11, no. 1, pp. 11-18.
P. T. Selvy et al., "A proficient clustering technique to detect CSF level in MRI brain images using PSO algorithm," WSEAS Trans. Comput., vol. 7, no. 7, pp. 298-308, 2013.
F. Segovia et al., "Combining PET images and neuropsychological test data for automatic diagnosis of Alzheimer's disease," PLoS One, vol. 9, no. 2, e88687, 2014.
S. J. Hussain, T. S. Savithri, and P. V. S. Devi, "Segmentation of tissues in brain MRI images using dynamic neuro-fuzzy technique," International Journal of Soft Computing and Engineering, vol. 1, no. 6, pp. 2231-2307, 2012.
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