Adaptive comprehensive particle swarm optimisation‐based functional‐link neural network filtre model for denoising ultrasound images. Issue 6 (18th January 2021)

• Record Type: Journal Article
• Authors: Kumar, Manish; Mishra, Sudhansu Kumar; Joseph, Justin; Jangir, Sunil Kumar; Goyal, Dinesh
• Is Part Of: IET image processing. Volume 15:Issue 6(2021)
• Page Start: 1232
• Page End: 1246
• Publication Date: 2021-01-18
• Subjects: Sonic and ultrasonic radiation (medical uses) -- Patient diagnostic methods and instrumentation -- Other topics in statistics -- Optimisation techniques -- Optical, image and video signal processing -- Filtering methods in signal processing -- Sonic and ultrasonic radiation (biomedical imaging/measurement) -- Other topics in statistics -- Optimisation techniques -- Computer vision and image processing techniques -- Biology and medical computing; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12100
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: Multiplicative speckle is a dominant type of noise that spoils the inherent features of the medical ultrasound (US) images. Apart from the speckle, impulse and Gaussian noises also appear in the US image due to the error encountered during the data transmission and transition of switching circuits and sensors. The noise not only deteriorates the visual quality of the US but also creates complications in the diagnosis. In this study, an adaptive comprehensive particle swarm optimisation‐based functional‐link neural network (ACPSO‐FLNN) filtre has been proposed and implemented in filtering noisy US images in different noise conditions. The proposed filtre is compared with some state‐of‐the‐art filtering techniques. Quantitative and qualitative measures such as training time, time complexity, convergence rate, and statistical test are included to study the performance of the proposed filtre. Furthermore, sensitivity, computational complexity, and order of the proposed filtre are also investigated. Friedman's test with 50 images is performed for statistical validation. The lower rank, that is, 6 and critical value of 21 × 10–4 of the proposed ACPSO‐FLNN filtre validates its dominance over other filtres.