scholarly journals Rotational Wavelet Filters for Analysis of Brain MRI in Detection of Alzheimer’s Disease

2021 ◽  
Vol 12 (6) ◽  
pp. 2817-2825
Author(s):  
Prof. Preeti S. Topannavar Et al.
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Wavelet Transform ◽  
Brain Mri ◽  
Rate Sensitivity ◽  
Texture Features ◽  
Complex Wavelet Transform ◽  
Wavelet Filters ◽  
Transform Coefficients ◽  
Complex Wavelet

In this paper, a method is suggested for multi directional analysis of Magnetic Resonance Image (MRI) scans for detection of Alzheimer’s disease (AD). This is a novel technique which utilizes, two-dimensional (2-D) rotated complex wavelet filters (RCWF) for feature identification. DTCWT identifies the features in 6 directions (±150±450, ±750) while RCWT identifies the features in different 6 directions (-300,0, +300, +600, +900, +1200), which enhances the directional selectivity of the transform coefficients and results in well description of corresponding textures. Dual-tree rotated complex wavelet transform (DT- RCWF) and dual-tree complex wavelet transform (DT- CWT) are applied to the sample images at a time thus the transform coefficients in twelve different directions is obtained simultaneously. The obtained transform coefficients are used for calculation of various texture features such as energy, entropy and standard deviation. The obtained parameters form the feature vectors which are given as input to the classifiers to get the input classified as Normal control or AD sufferer. This proposed algorithm produces results which are superior in terms of accuracy, feature extraction rate, sensitivity, specificity, precision and recall necessary to realize the efficiency of diagnosis of Alzheimer’s Disease as compared to other existing methods.

scholarly journals Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Debesh Jha ◽  
Ji-In Kim ◽  
Goo-Rak Kwon
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Wavelet Transform ◽  
Feature Vector ◽  
Complex Wavelet Transform ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Complex Wavelet ◽  
Brain Data

Background. Error-free diagnosis of Alzheimer’s disease (AD) from healthy control (HC) patients at an early stage of the disease is a major concern, because information about the condition’s severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI) classification. However, distinguishing between Alzheimer’s brain data and healthy brain data in older adults (age > 60) is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT) for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA). The reduced feature vector is sent to feed-forward neural network (FNN) to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.

Compound Fault Feature Separation of Rolling Bearing Based on Complex Wavelet and Energy Operator Demodulation

2012 ◽  
Vol 226-228 ◽  
pp. 765-771
Author(s):  
Yang Yang ◽  
Jian Yu Zhang ◽  
Sui Zheng Zhang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Coefficient ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Difficult Problem ◽  
Transform Domain ◽  
Complex Wavelet Transform ◽  
Transform Coefficients ◽  
Complex Wavelet ◽  
Fault Information

Compound fault feature separation is a difficult problem in diagnosis field of mechanical system. For the rolling bearing with compound fault on outer and inner race, feature separation technology based on complex wavelet transform and energy operator demodulation is introduced. Through continuous wavelet transform, coefficients of mixed fault signal can be achieved in different wavelet transform domain (i.e. real, imaginary, modulus and phase domain). Furthermore, wavelet power spectrum contours and time average wavelet energy spectrum are applied to extract the scales which hold rich fault information, and the wavelet coefficient slice of specific scale is also drawn. For wavelet coefficients in different domain, spectrum analysis and energy operator demodulation can be used successfully to separate mixed fault. The comparison of feature extraction effect between complex wavelet and real wavelet transform shows that complex wavelet transform is obviously better than the latter.

Color Image Retrieval Algorithm Based on Dual Tree Complex Wavelet Transform

2013 ◽  
Vol 427-429 ◽  
pp. 1761-1764
Author(s):  
Xiao Li Zhao ◽  
Shu Jun Yin
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Color Image ◽  
Texture Features ◽  
Retrieval Algorithm ◽  
Complex Wavelet Transform ◽  
Invariant Moments ◽  
Frequency Components ◽  
Complex Wavelet ◽  
Form Feature

A color image retrieval algorithm was presented in order to retrieve image from massive images. This method firstly transformed image from RGB space to HSV space. V component of HSV space was decomposed into three levels by dual tree complex wavelet transform (DT-CWT) to extract high frequency components as texture features. Invariant moments of V component were extracted as shape features. Information of H component of HSV space was extracted as color features. Then three types of features were combined to form feature vector to judge similarity of images. Experiments show that this algorithm has not only high precision and recall but also low time consumption.

Quantum and dual-tree complex wavelet transform-based image watermarking

2020 ◽  
Vol 34 (04) ◽  
pp. 2050009 ◽  
Author(s):  
Deepika Ghai ◽  
Hemant Kumar Gianey ◽  
Arpit Jain ◽  
Raminder Singh Uppal
Keyword(s):  
Wavelet Transform ◽  
Performance Metrics ◽  
Image Watermarking ◽  
Multimedia Data ◽  
Watermark Image ◽  
Complex Wavelet Transform ◽  
Public Networks ◽  
Transform Coefficients ◽  
Complex Wavelet ◽  
Value Decomposition

Nowadays, multimedia applications are extensively utilized and communicated over Internet. Due to the use of public networks for communication, the multimedia data are prone to various security attacks. In the past few decades, image watermarking has been extensively utilized to handle this issue. Its main objective is to embed a watermark into a host multimedia data without affecting its presentation. However, the existing methods are not so effective against multiplicative attacks. Therefore, in this paper, a novel quantum-based image watermarking technique is proposed. It initially computes the dual-tree complex wavelet transform coefficients of an input cover image. The watermark image is then scrambled using Arnold transform. Thereafter, in the lower coefficient input the watermark image is embedded using quantum-based singular value decomposition (SVD). Finally, the covered image is obtained by applying the inverse dual-tree complex wavelet transform on the obtained coefficients. Comparative analyses are carried out by considering the proposed and the existing watermarking techniques. It has been found that the proposed technique outperforms existing watermarking techniques in terms of various performance metrics.

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