文章基本信息

标题：Feature extraction algorithms from MRI to evaluate quality parameters on meat products by using data mining
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作者：Daniel Caballero
期刊名称：ELCVIA: electronic letters on computer vision and image analysis
印刷版ISSN：1577-5097
出版年度：2018
卷号：16
期号：2
页码：1-4
DOI：10.5565/rev/elcvia.1100
语种：English
出版社：Centre de Visió per Computador
摘要：This thesis proposes a new methodology to determine the quality characteristics of meat products (Iberian loin and ham) in a non-destructive way. For that, new algorithms have been developed to analyze Magnetic Resonance Imaging (MRI), and data mining techniques have been applied on data obtained from the images. The general procedure consists of obtaining MRI of meat products, and applying different computer vision algorithms (texture and fractal approaches, mainly), which allow the extraction of sets of computational features. Figure 1 shows the design of the proposed procedure. To achieve this, different research have been done, based on: high-field and low-field MRI scanners different acquisition sequences: Spin Echo (SE), Gradient Echo (GE) and Turbo 3D (T3D) different texture approaches: Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM) and Neighboring Gray Level Dependence Matrix (NGLDM) fractals algorithms: Classical Fractal Algorithm (CFA), Fractal Texture Algorithm (FTA) and One Point Fractal Texture Algorithm (OPFTA) FTA [1] and OPFTA [2] have been developed in this thesis. They allow analyzing MRI images, properly, noting OPFTA for its simplicity and lower computational cost. At the same time, the meat products, Iberian hams and loins, were also analyzed by means of physico-chemical and sensory techniques. Databases were constructed with all these data. Different data mining techniques have been applied on them: deductive (Multiple Linear Regression (MLR)) [3], classification (Decision Trees (DT) and Rules-based Systems (RBS)) [4], and prediction techniques [5-7]. Figure 2 shows the MRI images of fresh and dry-cured Iberian loins (Figure 2A and 2B) and fresh and dry-cured hams (Figure 2C and 2D). The accuracy of the analysis of the quality parameters of Iberian ham and loin is affected by the MRI acquisition sequence, the algorithm used to analyze them and the data mining technique applied. Considering the data mining techniques, MLR and DT are appropriate, respectively, to deduce physico-chemical parameters of hams, and to classify as a function of salt content in hams. Regarding to the predictive technique, MLR could be indicate it allows obtaining equations to determine the physico-chemical characteristics and sensory attributes of Iberian loins and hams with a high degree of reliability, and analyzing the quality of these meat products in a non-destructive, efficient, effective and accurate way.
其他摘要：This thesis proposes a new methodology to determine the quality characteristics of meat products (Iberian loin and ham) in a non-destructive way. For that, new algorithms have been developed to analyze Magnetic Resonance Imaging (MRI), and data mining techniques have been applied on data obtained from the images. The general procedure consists of obtaining MRI of meat products, and applying different computer vision algorithms (texture and fractal approaches, mainly), which allow the extraction of sets of computational features. Figure 1 shows the design of the proposed procedure. To achieve this, different research have been done, based on: high-field and low-field MRI scanners different acquisition sequences: Spin Echo (SE), Gradient Echo (GE) and Turbo 3D (T3D) different texture approaches: Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM) and Neighboring Gray Level Dependence Matrix (NGLDM) fractals algorithms: Classical Fractal Algorithm (CFA), Fractal Texture Algorithm (FTA) and One Point Fractal Texture Algorithm (OPFTA) FTA [1] and OPFTA [2] have been developed in this thesis. They allow analyzing MRI images, properly, noting OPFTA for its simplicity and lower computational cost. At the same time, the meat products, Iberian hams and loins, were also analyzed by means of physico-chemical and sensory techniques. Databases were constructed with all these data. Different data mining techniques have been applied on them: deductive (Multiple Linear Regression (MLR)) [3], classification (Decision Trees (DT) and Rules-based Systems (RBS)) [4], and prediction techniques [5-7]. Figure 2 shows the MRI images of fresh and dry-cured Iberian loins (Figure 2A and 2B) and fresh and dry-cured hams (Figure 2C and 2D). The accuracy of the analysis of the quality parameters of Iberian ham and loin is affected by the MRI acquisition sequence, the algorithm used to analyze them and the data mining technique applied. Considering the data mining techniques, MLR and DT are appropriate, respectively, to deduce physico-chemical parameters of hams, and to classify as a function of salt content in hams. Regarding to the predictive technique, MLR could be indicate it allows obtaining equations to determine the physico-chemical characteristics and sensory attributes of Iberian loins and hams with a high degree of reliability, and analyzing the quality of these meat products in a non-destructive, efficient, effective and accurate way.