TY - JOUR
T1 - Application of Image Texture Analysis for Evaluation of X-Ray Images of Fungal-Infected Maize Kernels
AU - Orina, Irene
AU - Manley, Marena
AU - Kucheryavskiy, Sergey V.
AU - Williams, Paul
PY - 2018
Y1 - 2018
N2 - The feasibility of image texture analysis to evaluate X-ray images of fungal-infected maize kernels was investigated. X-ray images of maize kernels infected with Fusarium verticillioides and control kernels were acquired using high-resolution X-ray micro-computed tomography. After image acquisition and pre-processing, several algorithms were developed to extract image textural features from selected two-dimensional (2D) images of the kernels. Four first-order statistics (mean, standard deviation, kurtosis and skewness) and four grey level co-occurrence matrix (GLCM) features (correlation, energy, homogeneity and contrast) were extracted from the side, front and top views of each kernel and used as inputs for principal component analysis (PCA). The first-order statistical image features gave a better separation of the control from infected kernels on day 8 post-inoculation. Classification models were developed using partial least squares discriminant analysis (PLS-DA), and accuracies of 67 and 73% were achieved using first-order statistical features and GLCM extracted features, respectively. This work provides information on the possible application of image texture as method for analysing X-ray images.
AB - The feasibility of image texture analysis to evaluate X-ray images of fungal-infected maize kernels was investigated. X-ray images of maize kernels infected with Fusarium verticillioides and control kernels were acquired using high-resolution X-ray micro-computed tomography. After image acquisition and pre-processing, several algorithms were developed to extract image textural features from selected two-dimensional (2D) images of the kernels. Four first-order statistics (mean, standard deviation, kurtosis and skewness) and four grey level co-occurrence matrix (GLCM) features (correlation, energy, homogeneity and contrast) were extracted from the side, front and top views of each kernel and used as inputs for principal component analysis (PCA). The first-order statistical image features gave a better separation of the control from infected kernels on day 8 post-inoculation. Classification models were developed using partial least squares discriminant analysis (PLS-DA), and accuracies of 67 and 73% were achieved using first-order statistical features and GLCM extracted features, respectively. This work provides information on the possible application of image texture as method for analysing X-ray images.
KW - image texture analysis
KW - fungal invasion
KW - X-Ray microtomography
KW - maize
KW - GLCM
UR - http://www.scopus.com/inward/record.url?scp=85046034656&partnerID=8YFLogxK
U2 - 10.1007/s12161-018-1251-9
DO - 10.1007/s12161-018-1251-9
M3 - Journal article
SN - 1936-9751
VL - 11
SP - 2799
EP - 2815
JO - Food Analytical Methods
JF - Food Analytical Methods
IS - 10
ER -