Alzheimer’s disease (AD) is a common form of senile dementia. Although the understanding of key steps underlying neurodegeneration in Alzheimer’s disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by patient. Conventional clinical decision making systems are more manual in nature and ultimate conclusion in terms of exact diagnosis is remote. In this case, the employment of advanced Biomedical Engineering Technology will definitely helpful for making diagnosis. Any disease modifying treatments which are developed are most possibly to be achieving success if initiated early in the process, and this needs that we tend to develop reliable, validated and economical ways to diagnose Alzheimer’s kind pathology. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. Profiling of human body parameter using computers can be utilised for the early diagnosis of Alzheimer’s disease. There are several imaging techniques used in clinical practice for the diagnosis of Alzheimer’s type pathology. There are lot of tests and neuroimaging modalities to be performed for an effective diagnosis of the disease. Prominent of them are Magnetic Resonance Imaging Scan (MRI), Positron Emission Tomography (PET), Single Photon Emission CT Scanning (SPECT), MRI Imaging and Optical Coherence Tomography (OCT). In this research we have proposed a new scheme based on Wavelet Networks (WN) for the feature extraction of MRI brain images for the early diagnosis of AD. The database of MRI images were obtained from Sree Gokulam Medical College and Research Foundation (SGMC&RF), Trivandrum, India.
| Published in | Bioprocess Engineering (Volume 1, Issue 2) |
| DOI | 10.11648/j.be.20170102.11 |
| Page(s) | 35-42 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
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Copyright © The Author(s), 2017. Published by Science Publishing Group |
Alzheimer Disease, Dementia, Wavelet Networks, Sombrero, MRI Image, Early Diagnosis
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APA Style
Sandeep C. S., Sukesh Kumar A., K. Mahadevan, Manoj P. (2017). Feature Extraction of MRI Brain Images for the Early Detection of Alzheimer’s Disease. Bioprocess Engineering, 1(2), 35-42. https://doi.org/10.11648/j.be.20170102.11
ACS Style
Sandeep C. S.; Sukesh Kumar A.; K. Mahadevan; Manoj P. Feature Extraction of MRI Brain Images for the Early Detection of Alzheimer’s Disease. Bioprocess Eng. 2017, 1(2), 35-42. doi: 10.11648/j.be.20170102.11
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Download@article{10.11648/j.be.20170102.11,
author = {Sandeep C. S. and Sukesh Kumar A. and K. Mahadevan and Manoj P.},
title = {Feature Extraction of MRI Brain Images for the Early Detection of Alzheimer’s Disease},
journal = {Bioprocess Engineering},
volume = {1},
number = {2},
pages = {35-42},
doi = {10.11648/j.be.20170102.11},
url = {https://doi.org/10.11648/j.be.20170102.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.be.20170102.11},
abstract = {Alzheimer’s disease (AD) is a common form of senile dementia. Although the understanding of key steps underlying neurodegeneration in Alzheimer’s disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by patient. Conventional clinical decision making systems are more manual in nature and ultimate conclusion in terms of exact diagnosis is remote. In this case, the employment of advanced Biomedical Engineering Technology will definitely helpful for making diagnosis. Any disease modifying treatments which are developed are most possibly to be achieving success if initiated early in the process, and this needs that we tend to develop reliable, validated and economical ways to diagnose Alzheimer’s kind pathology. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. Profiling of human body parameter using computers can be utilised for the early diagnosis of Alzheimer’s disease. There are several imaging techniques used in clinical practice for the diagnosis of Alzheimer’s type pathology. There are lot of tests and neuroimaging modalities to be performed for an effective diagnosis of the disease. Prominent of them are Magnetic Resonance Imaging Scan (MRI), Positron Emission Tomography (PET), Single Photon Emission CT Scanning (SPECT), MRI Imaging and Optical Coherence Tomography (OCT). In this research we have proposed a new scheme based on Wavelet Networks (WN) for the feature extraction of MRI brain images for the early diagnosis of AD. The database of MRI images were obtained from Sree Gokulam Medical College and Research Foundation (SGMC&RF), Trivandrum, India.},
year = {2017}
}
TY - JOUR T1 - Feature Extraction of MRI Brain Images for the Early Detection of Alzheimer’s Disease AU - Sandeep C. S. AU - Sukesh Kumar A. AU - K. Mahadevan AU - Manoj P. Y1 - 2017/06/26 PY - 2017 N1 - https://doi.org/10.11648/j.be.20170102.11 DO - 10.11648/j.be.20170102.11 T2 - Bioprocess Engineering JF - Bioprocess Engineering JO - Bioprocess Engineering SP - 35 EP - 42 PB - Science Publishing Group SN - 2578-8701 UR - https://doi.org/10.11648/j.be.20170102.11 AB - Alzheimer’s disease (AD) is a common form of senile dementia. Although the understanding of key steps underlying neurodegeneration in Alzheimer’s disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by patient. Conventional clinical decision making systems are more manual in nature and ultimate conclusion in terms of exact diagnosis is remote. In this case, the employment of advanced Biomedical Engineering Technology will definitely helpful for making diagnosis. Any disease modifying treatments which are developed are most possibly to be achieving success if initiated early in the process, and this needs that we tend to develop reliable, validated and economical ways to diagnose Alzheimer’s kind pathology. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. Profiling of human body parameter using computers can be utilised for the early diagnosis of Alzheimer’s disease. There are several imaging techniques used in clinical practice for the diagnosis of Alzheimer’s type pathology. There are lot of tests and neuroimaging modalities to be performed for an effective diagnosis of the disease. Prominent of them are Magnetic Resonance Imaging Scan (MRI), Positron Emission Tomography (PET), Single Photon Emission CT Scanning (SPECT), MRI Imaging and Optical Coherence Tomography (OCT). In this research we have proposed a new scheme based on Wavelet Networks (WN) for the feature extraction of MRI brain images for the early diagnosis of AD. The database of MRI images were obtained from Sree Gokulam Medical College and Research Foundation (SGMC&RF), Trivandrum, India. VL - 1 IS - 2 ER -
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