2022/10/11 by Michał Byra, Byra, Michal, Ziemowit Klimonda +3
Computer Science · Medicine · #AI in cancer detection #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Medical Physics (physics.med-ph) #Radiomics and Machine Learning in Medical Imaging #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2210.05833
openalex publication_date 2022/10/11 · openalex created_date 2022/10/14 · openalex updated_date 2026/07/28
Homodyned K (HK) distribution has been widely used to describe the scattering phenomena arising in various research fields, such as ultrasound imaging or optics. In this work, we propose a machine learning based approach to the estimation of the HK distribution parameters. We develop neural networks that can estimate the HK distribution parameters based on the signal-to-noise ratio, skewness and kurtosis calculated using fractional-order moments. Compared to the previous approaches, we consider the orders of the moments as trainable variables that can be optimized along with the network weights using the back-propagation algorithm. Networks are trained based on samples generated from the HK distribution. Obtained results demonstrate that the proposed method can be used to accurately estimate the HK distribution parameters.