Grupo de Ingeniería Estadística Multivariante GIEM

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Author: Ferrer Riquelme, Alberto José × Subject: Multivariate image analysis ×

Publication Search Results

Now showing 1 - 5 of 5
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    Prostate functional magnetic resonance image analysis using multivariate curve resolution methods
    (Wiley, 2014-08) Prats Montalbán, José Manuel; Sanz Requena, Roberto; Marti Bonmati, Luis; Ferrer Riquelme, Alberto José; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Ministerio de Ciencia e Innovación
    This paper discusses the potential of Multivariate Curve Resolution (MCR) models to extract physiological dynamics behaviors from Dynamic Contrast Enhanced Magnetic Resonance (DCE-MR) Imaging prostate perfusion studies for cancer diagnosis. A relationship with biomarkers ( hidden parameters for assessing the possible existence of a tumor) from pharmacokinetic models is also studied.
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    PLS: A versatile tool for industrial process improvement and optimization
    (John Wiley & Sons, 2008-12) Ferrer Riquelme, Alberto José; Aguado García, Daniel; Vidal Puig, Santiago; Prats Montalbán, José Manuel; Zarzo Castelló, Manuel; Dpto. de Ingeniería Hidráulica y Medio Ambiente; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Instituto Universitario de Ingeniería del Agua y del Medio Ambiente; Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos; Escuela Técnica Superior de Ingeniería Industrial; Escuela Técnica Superior de Ingeniería Informática; Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural; Grupo de Ingeniería Estadística Multivariante GIEM; European Commission; Ministerio de Ciencia y Tecnología
    [EN] Modern industrial processes are characterized by acquiring massive amounts of highly collinear data. In this context, partial least-squares (PLS) regression, if wisely used, can become a strategic tool for process improvement and optimization. In this paper we illustrate the versatility of this technique through several real case studies that basically differ in the structure of the X matrix (process variables) and Y matrix (response parameters). By using the PLS approach, the results show that it is possible to build predictive models (soft sensors) for monitoring the performance of a wastewater treatment plant, to help in the diagnosis of a complex batch polymerization process, to develop in automatic classifier based on image data, or to assist in the empirical model building of a continuous polymerization process.
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    Application of multivariate image analysis for on-line monitoring of a freeze-drying process for pharmaceutical products in vials
    (Elsevier, 2019) Colucci, D.; Prats Montalbán, José Manuel; Fisore, D.; Ferrer Riquelme, Alberto José; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Agencia Estatal de Investigación
    [EN] A new Process Analytical Technology (PAT) has been developed and tested for on-line process monitoring of a vacuum freeze-drying process. The sensor uses an infrared camera to obtain thermal images of the ongoing process and multivariate image analysis (MIA) to extract the information. A reference model was built and different kind of anomalous events were simulated to test the capacity of the system to promptly identify them. Two different data structures and two different algorithms for the imputation of the missing information have been tested and compared. Results show that the MIA-based PAT system is able to efficiently detect on-line undesired events occurring during the vacuum freeze-drying process.
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    Coupling 2D-wavelet decomposition and multivariate image analysis (2D WT-MIA)
    (John Wiley & Sons, 2018) Li Vigni, Mario; Prats Montalbán, José Manuel; Ferrer Riquelme, Alberto José; Cocchi, Marina; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM
    [EN] The use of 2D discrete wavelet transform in the feature enhancement phase of multivariate image analysis is discussed and implemented in a comparative way with respect to previous publications. In the proposed approach, all the resulting subimages obtained by discrete wavelet transform decomposition are unfolded pixel-wise and midlevel data fused to a feature matrix that is used for the feature analysis phase. Congruent subimages can be obtained either by reconstruction of each decomposition block to the original pixel dimensions or by using the stationary wavelet transform decomposition scheme. The main advantage is that all possible relationships among blocks, decomposition levels, and channels are assessed in a single multivariate analysis step (feature analysis). This is particularly useful in a monitoring context where the aim is to build multivariate control charts based on images. Moreover, the approach can be versatile for contexts where several images are analyzed at a time as well as in the multispectral image analysis. Both a set of simple artificial images and a set of real images, representative of the on-line quality monitoring context, will be used to highlight the details of the methodology and show how the wavelet transform allows extracting features that are informative of how strong the texture of the image is and in which direction it varies. 2D Wavelet Transform (DWT or SWT) in the Feature Enhancement phase of Multivariate Image Analysis is compared to current state of art. Wavelet-decomposition images are unfolded pixel-wise and mid-level datafused to a Feature Matrix so that all relationships among blocks, decomposition levels and channels are assessed in a single multivariate Feature Analysis step. The approach is suitable in process monitoring context. Also, denoising and background removal are obtained at WT decomposition stage, and it can be easily extended to hyperspectral images.
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    Multivariate image analysis: a review with applications
    (Elsevier, 2011-05) Prats-Montalbán, José Manuel; De Juan, A.; Ferrer Riquelme, Alberto José; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Ministerio de Ciencia e Innovación
    [EN] Nowadays, image analysis is becoming more important because of its ability to perform fast and non-invasive low-cost analysis on products and processes. Image analysis is a wide denomination that encloses classical studies on gray scale or RGB images, analysis of images collected using few spectral channels (sometimes called multispectral images) or, most recently, data treatments to deal with hyperspectral images, where the spectral direction is exploited in its full extension. Pioneering data treatments in image analysis were applied to simple images mainly for defect detection, segmentation and classification by the Computer Science community. From the late 80s, the chemometric community joined this field introducing powerful tools for image analysis, which were already in use for the study of classical spectroscopic data sets and were appropriately modified to fit the particular characteristics of image structures. These chemometric approaches adapt to images of all kinds, from the simplest to the hyperspectral images, and have provided new insights on the spatial and spectroscopic information of this kind of data sets. New fields open by the introduction of chemometrics on image analysis are exploratory image analysis, multivariate statistical process control (monitoring), multivariate image regression or image resolution. This paper reviews the different techniques developed in image analysis and shows the evolution in the information provided by the different methodologies, which has been heavily pushed by the increasing complexity of the image measurements in the spatial and, particularly, in the spectral direction. © 2011 Elsevier B.V.