A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment
Abstract Background The detection of Alzheimer’s Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts...
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| Format: | Article |
| Language: | English |
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BMC
2020-02-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-020-1055-x |
| _version_ | 1819278264483446784 |
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| author | Harsh Bhasin Ramesh Kumar Agrawal For Alzheimer’s Disease Neuroimaging Initiative |
| author_facet | Harsh Bhasin Ramesh Kumar Agrawal For Alzheimer’s Disease Neuroimaging Initiative |
| author_sort | Harsh Bhasin |
| collection | DOAJ |
| description | Abstract Background The detection of Alzheimer’s Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data. Methods This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine. Results The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data. Conclusion The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings. |
| first_indexed | 2024-12-24T00:09:15Z |
| format | Article |
| id | doaj.art-7db12f375315458789c5754dc1e47af3 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-24T00:09:15Z |
| publishDate | 2020-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-7db12f375315458789c5754dc1e47af32022-12-21T17:24:55ZengBMCBMC Medical Informatics and Decision Making1472-69472020-02-0120111010.1186/s12911-020-1055-xA combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairmentHarsh Bhasin0Ramesh Kumar Agrawal1For Alzheimer’s Disease Neuroimaging InitiativeSchool of Computer and Systems Sciences, Jawaharlal Nehru UniversitySchool of Computer and Systems Sciences, Jawaharlal Nehru UniversityAbstract Background The detection of Alzheimer’s Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data. Methods This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine. Results The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data. Conclusion The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.http://link.springer.com/article/10.1186/s12911-020-1055-xMild cognitive impairmentsMachine learning3D discrete wavelet transform3D local binary patternMagnetic resonance imaging |
| spellingShingle | Harsh Bhasin Ramesh Kumar Agrawal For Alzheimer’s Disease Neuroimaging Initiative A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment BMC Medical Informatics and Decision Making Mild cognitive impairments Machine learning 3D discrete wavelet transform 3D local binary pattern Magnetic resonance imaging |
| title | A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment |
| title_full | A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment |
| title_fullStr | A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment |
| title_full_unstemmed | A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment |
| title_short | A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment |
| title_sort | combination of 3 d discrete wavelet transform and 3 d local binary pattern for classification of mild cognitive impairment |
| topic | Mild cognitive impairments Machine learning 3D discrete wavelet transform 3D local binary pattern Magnetic resonance imaging |
| url | http://link.springer.com/article/10.1186/s12911-020-1055-x |
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