Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control.

This paper discusses current formulations based on fuzzy-logic control concepts as applied to the removal of impulsive noise from digital images. We also discuss the various principles related to fuzzy-ruled based logic control techniques, aiming at preserving edges and digital image details efficiently. Detailed descriptions of a number of formulations for recently developed fuzzy-rule logic controlled filters are provided, highlighting the merit of each filter.

A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images.

In this paper, we present rule-based fuzzy inference systems that consist of a series of mathematical representations based on fuzzy concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms.

MRI radiogenomics for intelligent diagnosis of breast tumors and accurate prediction of neoadjuvant chemotherapy responses-a review.

Background And Objective: This paper aims to overview multidimensional mining algorithms in relation to Magnetic Resonance Imaging (MRI) radiogenomics for computer aided detection and diagnosis of breast tumours. The work also aims to address a new problem in radiogenomics mining: how to combine structural radiomics information with non-structural genomics information for improving the accuracy and efficacy of Neoadjuvant Chemotherapy (NAC).

Methods: This requires the automated extraction of parameters from non-structural breast radiomics data, and finding feature vectors with diagnostic value, which then are combined with genomics data.

Video-based fractional order identification of diffusion dynamics for the analysis of migration rates of polar and nonpolar liquids: Water and oil studies.

Diffusion dynamics of water and oil are observed in real-time using video images. Savitzky-Golay derivative filtering ensures accurate localization of the liquid front. System identification demonstrates that a fractional-order model with only two parameters may be used to describe the dynamics of the diffusion process.

Artificial Intelligence in Breast MRI Radiogenomics: Towards Accurate Prediction of Neoadjuvant Chemotherapy Responses.

Neoadjuvant Chemotherapy (NAC) in breast cancer patients has considerable prognostic and treatment potential and can be tailored to individual patients as part of precision medicine protocols. This work reviews recent advances in artificial intelligence so as to enable the use of radiogenomics for accurate NAC analysis and prediction. The work addresses a new problem in radiogenomics mining: How to combine structural radiomics information and non-structural genomics information for accurate NAC prediction.

Identification of fractional-order transfer functions using exponentially modulated signals with arbitrary excitation waveforms.

This paper proposes a new identification method based on an exponential modulation scheme for the determination of the coefficients and exponents of a fractional-order transfer function. The proposed approach has a broader scope of application compared to a previous method based on step response data, in that it allows for the use of arbitrary input signals. Moreover, it dispenses with the need for repeated simulations during the search for the best fractional exponents, which significantly reduces the computational workload involved in the identification process.

Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers.

This work provides a performance comparison of four different machine learning classifiers: multinomial logistic regression with ridge estimators (MLR) classifier, k-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) as applied to terahertz (THz) transient time domain sequences associated with pixelated images of different powder samples. The six substances considered, although have similar optical properties, their complex insertion loss at the THz part of the spectrum is significantly different because of differences in both their frequency dependent THz extinction coefficient as well as differences in their refractive index and scattering properties. As scattering can be unquantifiable in many spectroscopic experiments, classification solely on differences in complex insertion loss can be inconclusive.

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

Objective: 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.

Terahertz deconvolution.

The ability to retrieve information from different layers within a stratified sample using terahertz pulsed reflection imaging and spectroscopy has traditionally been resolution limited by the pulse width available. In this paper, a deconvolution algorithm is presented which circumvents this resolution limit, enabling deep sub-wavelength and sub-pulse width depth resolution. The algorithm is explained through theoretical investigation, and demonstrated by reconstructing signals reflected from boundaries in stratified materials that cannot be resolved directly from the unprocessed time-domain reflection signal.

Optimization of apodization functions in terahertz transient spectrometry.

A quadratic programming optimization procedure for designing asymmetric apodization windows tailored to the shape of time-domain sample waveforms recorded using a terahertz transient spectrometer is proposed. By artificially degrading the waveforms, the performance of the designed window in both the time and the frequency domains is compared with that of conventional rectangular, triangular (Mertz), and Hamming windows. Examples of window optimization assuming Gaussian functions as the building elements of the apodization window are provided.

Optimal discrimination and classification of THz spectra in the wavelet domain.

In rapid scan Fourier transform spectrometry, we show that the noise in the wavelet coefficients resulting from the filter bank decomposition of the complex insertion loss function is linearly related to the noise power in the sample interferogram by a noise amplification factor. By maximizing an objective function composed of the power of the wavelet coefficients divided by the noise amplification factor, optimal feature extraction in the wavelet domain is performed. The performance of a classifier based on the output of a filter bank is shown to be considerably better than that of an Euclidean distance classifier in the original spectral domain.

Analysis of spectroscopic measurements of leaf water content at terahertz frequencies using linear transforms.

We provide a unified framework for a range of linear transforms that can be used for the analysis of terahertz spectroscopic data, with particular emphasis on their application to the measurement of leaf water content. The use of linear transforms for filtering, regression, and classification is discussed. For illustration, a classification problem involving leaves at three stages of drought and a prediction problem involving simulated spectra are presented.