&lt;title&gt;Multiresolution image denoising based on wavelet transform&lt;/title&gt;

Hassanien, Aboul Ella; Henawy, Ibrahim El; Own, Hala S.

doi:10.1117/12.449725

Cited by 2 publications

(2 citation statements)

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“…Then, this adjusted image is segmented using PCNN and the segmented image boundary is added to the original image. The third main step of the diagnosis process is the extraction of ten features, such as the contrast, the correlation and the dissimilarity, using wavelet-based analysis (Hassanien et al, 2001). Finally, these features are used to classify the images as normal or abnormal with SVM, a method which seeks the optimal separating hyper plane between two classes by focusing on the training samples that lie on the class boundaries while discarding other training samples effectively.…”

Section: Applications Of Neural Network In Disease Diagnosismentioning

confidence: 99%

State-of-the-Art Neural Networks Applications in Biology

Filntisi

Papangelopoulos

Bencúrová

et al. 2013

International Journal of Systems Biology and Biomedical Technologies

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Artificial neural networks (ANNs) are a well-established computational method inspired by the structure and function of biological central nervous systems. Since their conception, ANNs have been utilized in a vast variety of applications due to their impressive information processing abilities. A vibrant field, ANNs have been utilized in bioinformatics, a general term for describing the combination of informatics, biology and medicine. This article is an effort to investigate recent advances in the area of bioinformatical applications of ANNs, with emphasis in disease diagnosis, genetics, proteomics, and chemoinformatics. The combination of neural networks and game theory in some of these application is also discussed.

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Section: Applications Of Neural Network In Disease Diagnosismentioning

confidence: 99%

State-of-the-Art Neural Networks Applications in Biology

Filntisi

Papangelopoulos

Bencúrová

et al. 2013

International Journal of Systems Biology and Biomedical Technologies

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“…It has been shown to have good properties of time and frequency localization and is robust to time varying signal analysis. The wavelet coefficients represent measures of similarity of the local shape of the signal to the mother wavelet under different shifts and scales [43]. Wavelets with their multiresolution property, have been proved to be effective in the integrating of coarse features and finer resolution details of source images to produce a good fused image.…”

Section: Processingmentioning

confidence: 99%

An overview of rough-hybrid approaches in image processing

Hassanien

Abraham

Peters

et al. 2008

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)

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Abstract-Rough set theory offers a novel approach to manage uncertainty that has been used for the discovery of data dependencies, importance of features, patterns in sample data, feature space dimensionality reduction, and the classification of objects. Consequently, rough sets have been successfully employed for various image processing tasks including image segmentation, enhancement and classification. Nevertheless, while rough sets on their own provide a powerful technique, it is often the combination with other computational intelligence techniques that results in a truly effective approach. In this paper we show how rough sets have been combined with various other methodologies such as neural networks, wavelets, mathematical morphology, fuzzy sets, genetic algorithms, bayesian approaches, swarm optimization, and support vector machines in the image processing domain.

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<title>Multiresolution image denoising based on wavelet transform</title>

Cited by 2 publications

References 0 publications

State-of-the-Art Neural Networks Applications in Biology

State-of-the-Art Neural Networks Applications in Biology

An overview of rough-hybrid approaches in image processing

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