Browsing by Author "Iriondo, Pedro M."
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Item 3D Active Surfaces for Liver Segmentation in Multisequence MRI Images(2016-08-01) Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro M.; COMPUTER_VISION; Tecnalia Research & InnovationBiopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59.Item Automatic 3D model-based method for liver segmentation in MRI based on Active Contours and Total Variation minimization(2015-07-01) Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro M.; COMPUTER_VISION; Tecnalia Research & InnovationLiver cancer is one of the leading causes of cancer-related mortality worldwide. Non-invasive techniques of medical imaging such as Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) are often used by radiologists for diagnosis and surgery planning. With the aim of assuring the most reliable intervention planning to surgeons, new accurate methods and tools must be provided to locate and segment the regions of interest. Automated liver segmentation is a challenging problem for which promising results have been achieved mostly for CT. However, MRI is required by radiologists, since it offers better information for diagnosis purposes. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise, low contrast and poorly defined edges of the liver in relation to adjacent organs. In this paper, we present a method for MRI automatic 3D liver segmentation by means of an Active Contour model extended to 3D and minimized by Total Variation dual approach that has also been extended to 3D. A new approach to enhance the contrast in the input MRI image is proposed and it allows more accurate segmentation. The proposed methodology allows replacing the input image by a probability map obtained by means of a previously generated statistical model of the liver. An Accuracy of 98.89 and Dice Similarity Coefficient of 90.19 are in line with other state-of-the-art methodologies.Item Biologically-inspired data decorrelation for hyper-spectral imaging(2011) Picon, Artzai; Ghita, Ovidiu; Rodriguez-Vaamonde, Sergio; Iriondo, Pedro M.; Whelan, PF; Tecnalia Research & InnovationHyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classificationItem Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data(2009-11) Picon, Artzai; Ghita, Ovidiu; Whelan, Paul F.; Iriondo, Pedro M.; Whelan, PF; COMPUTER_VISIONHyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (FUzzy Spectral and Spatial classifiER) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyperspectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyperspectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for nonferrous material classification.Item Real-time hyperspectral processing for automatic nonferrous material sorting(2012) Picon, Artzai; Ghita, Ovidiu; Bereciartua, Arantza; Echazarra, Jone; Whelan, PF; Iriondo, Pedro M.The application of hyperspectral sensors in the development of machine vision solutions has become increasingly popular as the spectral characteristics of the imaged materials are better modeled in the hyperspectral domain than in the standard trichromatic red, green, blue data. While there is no doubt that the availability of detailed spectral information is opportune as it opens the possibility to construct robust image descriptors, it also raises a substantial challenge when this high-dimensional data is used in the development of real-time machine vision systems. To alleviate the computational demand, often decorrelation techniques are commonly applied prior to feature extraction. While this approach has reduced to some extent the size of the spectral descriptor, data decorrelation alone proved insufficient in attaining real-time classification. This fact is particularly apparent when pixel-wise image descriptors are not sufficiently robust to model the spectral characteristics of the imaged materials, a case when the spatial information (or textural properties) also has to be included in the classification process. The integration of spectral and spatial information entails a substantial computational cost, and as a result the prospects of real-time operation for the developed machine vision system are compromised. To answer this requirement, in this paper we have reengineered the approach behind the integration of the spectral and spatial information in the material classification process to allow the real-time sorting of the nonferrous fractions that are contained in the waste of electric and electronic equipment scrap.