An Improved Multivariate Image Analysis Method for Quality Control of Nanofiber Membranes

Some special topics in multivariate image analysis

Multi-way methods in image analysis—relationships and applications

Application of partial least squares and radial basis function neural networks in multivariate imaging analysis-quantitative structure activity relationship: Study of cyclin dependent kinase 4 inhibitors

Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimer's disease

In several process industries including mineral processing, where the materials are solids or slurries, some important measurements cannot be obtained using standard instrumentation (e.g., flow, temperature, pressure, pH, power draw, etc.), but can be visually appraised, and could be automatically quantified using machine vision techniques. In general, the information to extract from process images is not well defined and is stochastic in nature compared with those typically encountered in the manufacturing industry for automatic inspection, which have well defined deterministic features. Multivariate image analysis (MIA) as well as Multiresolution analysis (MRA) have been shown to be very efficient methods for spectral/textural analysis of process images. The objective of this chapter is to illustrate these methods using three mineral processing problems: (1) on-line estimation of froth grade in a flotation process; (2) flotation froth health monitoring for appropriate reagent dosage; and (3) on-line estimation of run-of-mine ore lithotype composition on conveyor belts. In all cases, the extracted image information could be used for developing new vision sensors for advanced control of mineral processing plants.

The Society of Biomolecular Imaging and Informatics First Annual Conference

Edibility and species discrimination of wild bolete mushrooms using FT-NIR spectroscopy combined with DD-SIMCA and RF models

fMRI has provided a new option to study cognitive phenomena. Recent developments in medical image processing and analysis allow researchers to study more elaborate cognitive tasks from a wide perspective. These techniques include Statistical Parametric Mapping, Subspace Modeling and Maximum Likelihood Estimation, and Spatio-temporal Analysis using Random Fields. Their common weakness is the assumption of the statistical independence among the image pixels. We have developed a multivariate segmentation method to functional MRI analysis for human brain function study based on the second-order statistics of images. It consists of four steps: (1) detecting the number of the distinctive image regions, (2) generating the scores and determining their rank, (3) forming score plots and clustering in the feature space, (4) projecting clusters from the feature space to the image space to generate object images. We have validated this method on the simulated and fMRI images. The theoretical and experimental results obtained by using this method were in good agreement. The relations between this method and other multivariate image analysis methods are discussed.

Flood events consist of flood peaks and flood volumes that are mutually correlated and need to be described by multivariate analysis methods, of which the copula functions are most desirable. Until now, the multivariate flood frequency analysis methods based on copulas does not consider the historical floods or paleological information. This may underestimate or overestimate the flood quantiles or conditional probabilities corresponding to high return periods, especially when the length of gauged record data series is relatively short. In this paper, a modified inference functions for margins (MIFM) method is proposed and used to estimate the parameters of both marginal distribution and joint distribution with incorporation of historical information. The conditional probabilities of flood volumes given that the peak discharge exceeding various values were derived. The Three Gorges reservoir (TGR) in China was selected as a case study. The bivariate flood quantiles were obtained based on bivariate return period and compared with current univariate design values. It is shown that the proposed method provides an alternative way for multivariate frequency analysis with historical information.

In situ particle size estimation for crystallization processes by multivariate image analysis

Estimation of bubble size distributions and shapes in two-phase bubble column using image analysis and optical probes

We recently established a high-speed, label-free, spectral imaging method based on stimulated Raman scattering (SRS). This method enables examination of cellular features within relatively short periods, thus enabling new imaging applications in pathology. Previously, we reported on label-free visualization of mouse tissue using SRS spectral microscopy combined with multivariate image analysis, but the feasibility of applying this approach to diseased tissues with diverse morphology and irregular chemical species has not been examined. We, therefore, assessed acetaminophen-induced liver injury to evaluate the potential use of Raman spectral microscopy for visualizing histopathologic specimens. Acetaminophen-overdosed mouse liver was prepared and the pathologic changes including centrilobular necrosis were confirmed. Multi-colored images were reconstructed through principal component analysis (PCA) of a multi-band SRS dataset, which provided rich information compared with a monochrome single-band SRS dataset. A wide view of the multi-colored principal component (PC) images showed the distribution of cellular constituents, which was similar to that observed by fat staining. In addition, different types of cells in liver parenchyma were also demonstrated. In conclusion, the combination of SRS spectral microscopy and PCA has the potential to reveal both the morphological and chemical features of specimens and therefore has potential utility in diagnostic pathology.

Comparative study of different approaches for multivariate image analysis in HPTLC fingerprinting of natural products such as plant resin

Multivariate image analysis (MIA) aims to extract useful information from multivariate image structures such as hypercubes. Multivariate image regression is particularly useful for the development of calibration models for quantitative analysis of hypercubes. The key step in multivariate calibration is model selection; this includes the selection of method (e.g. partial least squares or ridge regression) and setting of parameters within the selected method (e.g. number of latent variables or ridge parameter). Sub-optimal selection of method and or parameters generally leads to poor predictive performance on future samples. While conventional methods such as cross-validation are indispensible in this task, the inherent nature of hyperspectral images may also be exploited. Application of a given calibration model to each pixel in a hypercube results in a prediction map whose features may be used to evaluate the model itself. This paper explores the application of various image statistics extracted from prediction images as model performance metrics for MIA.

The multivariate statistical analysis methods, using both multiple linear regression(MLR) and partial least square(PLS), have been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PET) synthesis. On the basis of the set of data including the flow rate of water vapor, the flow rate of EG vapor, the concentration of acid end groups of a product and other operating conditions such as temperature, pressure, reaction times and feed monomer mole ratio, two multi-variable analysis methods have been applied. Their regression and prediction abilities also have been compared. The prediction results are critically compared with the actual plant data and the other mathematical model based results in reliability. This paper shows that PLS method approach can be used for the reasonably accurate prediction of a product quality of a direct esterification reactor in PET synthesis process.