A novel optimised method for speckle reduction in medical ultrasound images
Grey wolf optimisation; Hybrid filter; Kuan filter; Multiplicative noise; Non-local means filter; Performance metrics; Spatial filter; Speckle noise; Transform domain filter; Ultrasound image
The advancement of medical imaging techniques evolving from X-ray to PET images and the medical image analysis helped medical experts to detect, diagnose and offer treatments for complex disorders and deadly diseases in the human body. Among the various modalities used, Ultrasound imaging is the most widely accepted modality because of its affordability, non-invasive nature and various other features. But the presence of speckle noise in ultrasound image lowers the image quality and reduces diagnostic value. This article states an improved hybrid speckle noise reduction method, a combined application of Kuan and non-local means filters. In this method, Kuan filter is used to sharpen the edges and thereafter the speckle noise elimination is done by using the non-local means. In addition, the performance of the proposed hybrid filter and its design parameters are optimised by using a meta-heuristic called grey wolf optimiser. The performance of hybrid method is evaluated by analysing a chosen set of well-known post filtering methods used for speckle reduction with given ultrasound B-mode images. The comparison of test results using remarkable performance metrics and computation time demonstrate that the hybrid method can be used as the efficient speckle reduction method for image analysis. Copyright 2022 Inderscience Enterprises Ltd.
International Journal of Automation and Control, Vol-16, No. 2, pp. 137-163.
https://doi.org/10.1504/IJAAC.2022.121123 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125878615&doi=10.1504%2fIJAAC.2022.121123&partnerID=40&md5=b22cc6c8830280f7b9fb983ca1ec516e
Shereena V.B., Department of Computer Applications, MES College, Marampally, Kochi, India; Raju G., Department of Data Science, CHRIST (Deemed to be University), Lavasa Campus, Pune, India