Identifying Degenerative Brain Disease Using Rough Set Classifier Based on Wavelet Packet Method

Journal of Clinical Medicine
Ching-Hsue Cheng, Wei-Xiang Liu

Abstract

Population aging has become a worldwide phenomenon, which causes many serious problems. The medical issues related to degenerative brain disease have gradually become a concern. Magnetic Resonance Imaging is one of the most advanced methods for medical imaging and is especially suitable for brain scans. From the literature, although the automatic segmentation method is less laborious and time-consuming, it is restricted in several specific types of images. In addition, hybrid techniques segmentation improves the shortcomings of the single segmentation method. Therefore, this study proposed a hybrid segmentation combined with rough set classifier and wavelet packet method to identify degenerative brain disease. The proposed method is a three-stage image process method to enhance accuracy of brain disease classification. In the first stage, this study used the proposed hybrid segmentation algorithms to segment the brain ROI (region of interest). In the second stage, wavelet packet was used to conduct the image decomposition and calculate the feature values. In the final stage, the rough set classifier was utilized to identify the degenerative brain disease. In verification and comparison, two experiments were employed to verify t...Continue Reading

References

Jun 7, 2003·Computers in Biology and Medicine·Ibrahim TurkogluErdogan Ilkay
Feb 8, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·J FanW G Aref
Jan 5, 2011·NeuroImage·Roman FilipovychUNKNOWN Alzheimer's Disease Neuroimaging Initiative
Apr 8, 2011·Radiology·Linda K McEvoyUNKNOWN Alzheimer's Disease Neuroimaging Initiative
Nov 22, 2011·Brain Imaging and Behavior·Jing ZhangLongzheng Tong

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Citations

Nov 20, 2019·Journal of Clinical Medicine·Dat Tien NguyenKang Ryoung Park

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Methods Mentioned

BETA
nuclear magnetic resonance
imaging technique
feature extraction

Software Mentioned

TV
seg

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