Contribution to the Characterization of the Kidney Ultrasound Image Using Singularity Levels

Abstract

The aim to improve diagnosis decision systems of kidneys led to the concept of many methods of texture characterization of kidneys from ultrasound images. Here, as a first main contribution, we propose a texture characterization method based on the singularity defined by the Hölder exponent, which is multifractal local information. Indeed, the originality of our contribution here is to build a singularity-level matrix corresponding to different levels of regularities, from which we extract new texture features. Finally, as a second main contribution, we will evaluate the potential of our proposed multifractal features to characterize textured ultrasound images of the kidney. Having more reproducibility of the texture features first requires a good choice of Choquet capacity to calculate the irregularities and a selection of a more representative region of interest (ROIs) to analyze by carrying out an adapted virtual puncture in the kidney components. The results of the supervised classification, using three classes of images (young, healthy, and glomerulonephritis), are interesting and promising since the classification accuracy reaches about 80%. This encourages conducting further research to yield better results by overcoming the limitations and taking into account the recommendations made in this article.