Title: An improved speckle noise reduction scheme using switching and flagging of noisy data for pre-processing of ultrasonograms in detection of down syndrome during first and second trimesters

Authors: O. Jeba Shiney; J. Amar Pratap Singh; B. Priestly Shan

Addresses: School of Electrical, Electronics and Communication Engineering, Galgotias University, Greater Noida, India ' Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India ' School of Electrical, Electronics and Communication Engineering, Galgotias University, Greater Noida, India

Abstract: Down syndrome (DS) is reported to be one of the most common chromosomal abnormality affecting newborns all over the world. Diagnosis of the syndrome at an earlier stage during pregnancy will provide more options for the affected to make decisions on the interventional therapies required for the developing child. The techniques which are currently used in diagnosis of DS like amniocentesis and chorionic villus sampling (CVS) are invasive in nature and are associated with some percentage of risk. This paper aims at developing a clinical decision support system (CDSS) for detection of DS from ultrasound (US) foetal images. As a preliminary step in achieving this, the US images have to be denoised for removal of speckle noise. A modified mean median (MMM) filter has been proposed which is based on the principle of progressive switching theory. Experimental results show that the proposed filter provides better results in terms of peak signal to noise ratio (PSNR), image enhancement factor (IEF) and so on.

Keywords: ultrasound; down syndrome; modified mean median; MMM; amniocentesis; chorionic villus sampling; CVS; speckle noise; filter; diagnosis; clinical decision support system; CDSS; peak signal to noise ratio; PSNR.

DOI: 10.1504/IJBET.2021.119499

International Journal of Biomedical Engineering and Technology, 2021 Vol.37 No.2, pp.105 - 120

Received: 23 Jan 2018
Accepted: 12 Sep 2018
Published online: 08 Dec 2021 *

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