Literature DB >> 33816192

A denoising and enhancing method framework for 4D ultrasound images of human fetal heart.

Bin Liu1,2,3, Zhao Xu1, Qifeng Wang1, Xiaolei Niu1, Wei Xuan Chan4, Wiputra Hadi4, Choon Hwai Yap4,5.   

Abstract

BACKGROUND: 4D ultrasound images of human fetal heart are important for medical applications such as evaluation of fetal heart function and early diagnosis of congenital heart diseases. However, due to the high noise and low contrast characteristics in fetal ultrasound images, denoising and enhancements are important.
METHODS: In this paper, a special method framework for denoising and enhancing is proposed. It consists of a 4D-NLM (non-local means) denoising method for 4D fetal heart ultrasound image sequence, which takes advantage of context similar information in neighboring images to denoise the target image, and an enhancing method called the Adaptive Clipping for Each Histogram Pillar (ACEHP), which is designed to enhance myocardial spaces to distinguish them from blood spaces.
RESULTS: Denoising and enhancing experiments show that 4D-NLM method has better denoising effect than several classical and state-of-the-art methods such as NLM and WNNM. Similarly, ACEHP method can keep noise level low while enhancing myocardial regions better than several classical and state-of-the-art methods such as CLAHE and SVDDWT. Furthermore, in the volume rendering after the combined "4D-NLM+ACEHP" processing, the cardiac lumen is clear and the boundary is neat. The Entropy value that can be achieved by our method framework (4D-NLM+ACEHP) is 4.84.
CONCLUSIONS: Our new framework can thus provide important improvements to clinical fetal heart ultrasound images. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Entities:  

Keywords:  4D STIC human fetal heart ultrasound; Ultrasound image denoising and enhancing; histogram clipping; non-local mean

Year:  2021        PMID: 33816192      PMCID: PMC7930683          DOI: 10.21037/qims-20-818

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


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8.  Automatic Identification of Breast Ultrasound Image Based on Supervised Block-Based Region Segmentation Algorithm and Features Combination Migration Deep Learning Model.

Authors:  Wen-Xuan Liao; Ping He; Jin Hao; Xuan-Yu Wang; Ruo-Lin Yang; Dong An; Li-Gang Cui
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Authors:  Chen Ho-Chiang; Hsin Huang; Chih-Chung Huang
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