Improving energy compaction of a wavelet transform using genetic algorithm and fast neural network

Stolarek, Jan

doi:10.2478/v10170-010-0024-5

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…This is a nonparametric approach based on a wavelet representation of the logarithm of the power spectrum [113]. Alternative signal pre-processing methods for de-noising include base line correction [114], smoothing [115], first and second derivative [115,116], multiplicative scatter or (signal) correction [117], and standard normal variate analysis [118] Although all these methods have their own merit under different experimental conditions, one may argue that wavelet de-noising has the widest applicability [119].…”

Section: Analysis and Classification Of Thz Imaging Datamentioning

confidence: 99%

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

Yin

Hadjiloucas

Zhang

et al. 2016

Artificial Intelligence in Medicine

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We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods: Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation: Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results: Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion: The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community.

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Section: Analysis and Classification Of Thz Imaging Datamentioning

confidence: 99%

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

Yin

Hadjiloucas

Zhang

et al. 2016

Artificial Intelligence in Medicine

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show abstract

“…Then, a migration for optimal individuals among sub-spaces is run at a predefined number of generation. Since the synthesized wavelet composes much energy into low pass coefficients than the other does, then applying the proposed DWT to many levels should collect more energy in the same number of wavelet coefficients [32]. This paper implement DGA as a global optimization method to run on two consecutive processes.…”

Section: B Optimizing the Extracted Features Using Dgamentioning

confidence: 99%

“…Wavelet energy is the measure that keep the main characteristic of the wavelet coefficients and produce the same images with different translation, rotation and scale, having the same wavelet energy values [31,32]. Wavelet energy values are measured by analyzed iris image to its wavelet subimage coefficient (LLx, HLx, LHx, HHx) as defined in equations (3) [14] where w j k is the wavelet coefficients to subband at k-level.…”

Section: A Wavelet Decomposition Analysismentioning

confidence: 99%

Distributed Optimization Model of Wavelet Neuron for Human Iris Verification

Radwan¹,

Tarek²

2015

ijacsa

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Abstract-Automatic human iris verification is an active research area with numerous applications in security purposes. Unfortunately, most of feature extraction methods in human iris verification systems are sensitive to noise, scale and rotation. This paper proposes an integrated hybrid model among Discrete Wavelet Transform, Wavelet Neural Network and Genetic Algorithms for optimizing the feature extraction and verification methods. For any iris image, the wavelet features are extracted by Discrete Wavelet Transform without any dependency on scale and pixels' intensity. Besides, Wavelet Neural Network classifier is integrated as a local optimization method to solve the orientation problem and increase the intrinsic features. In solving the down sample process caused by DWT, each human iris should be characterized by a set of parameters of its optimal wavelet analysis function at a determined analysis level. Thus, distributed Genetic Algorithms, meta-heuristic algorithm, is introduced as a global optimization searching technique to discover the optimal parameter values. The details and limitation of this paper will be discussed where a comparative study should appear. Moreover, conclusions and future work are described.

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Digital Watermarking Enhancement Using Wavelet Filter Parametrization

Lipiński

Stolarek

2011

Lecture Notes in Computer Science

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Improving energy compaction of a wavelet transform using genetic algorithm and fast neural network

Cited by 5 publications

References 19 publications

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

Distributed Optimization Model of Wavelet Neuron for Human Iris Verification

Digital Watermarking Enhancement Using Wavelet Filter Parametrization

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