Computer-based quantitative image texture analysis using multi-collinearity diagnosis in chest X-ray images.

Despite tremendous efforts devoted to the area, image texture analysis is still an open research field. This paper presents an algorithm and experimental results demonstrating the feasibility of developing automated tools to detect abnormal X-ray images based on tissue attenuation. Specifically, this work proposes using the variability characterised by singular values and conditional indices extracted from the singular value decomposition (SVD) as image texture features. In addition, the paper introduces a "tuning weight" parameter to consider the variability of the X-ray attenuation in tissues affected by pathologies. This weight is estimated using the coefficient of variation of the minimum covariance determinant from the bandwidth yielded by the non-parametric distribution of variance-decomposition proportions of the SVD. When multiplied by the two features (singular values and conditional indices), this single parameter acts as a tuning weight, reducing misclassification and improving the classic performance metrics, such as true positive rate, false negative rate, positive predictive values, false discovery rate, area-under-curve, accuracy rate, and total cost. The proposed method implements an ensemble bagged trees classification model to classify X-ray chest images as COVID-19, viral pneumonia, lung opacity, or normal. It was tested using a challenging, imbalanced chest X-ray public dataset. The results show an accuracy of 88% without applying the tuning weight and 99% with its application. The proposed method outperforms state-of-the-art methods, as attested by all performance metrics.