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Updated: Nov 27, 2025
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Removal of manually induced artifacts in ultrasound images of thyroid nodules based on edge-connection and Criminisi
Ming Sun1, Qinglong Meng1, Ting Wang1
1Medical Engineering and Technology Research Center, Shandong First Medical University & Shandong Academy of Medical Sciences; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences.
Background And Objective:
There are various artificial markers in ultrasound images of thyroid nodules, which have impact on subsequent processing and computer-aided diagnosis. The purpose of this study was to develop an approach to automatically remove artifacts and restore ultrasound images of thyroid nodules.
Methods:
Fifty ultrasound images with manually induced artifacts were selected from publicly available and self-collected datasets. A combined approach was developed which consisted of two steps, artifacts detection and removal of the detected artifacts. Specifically, a novel edge-connection algorithm was used for artifact detection, detection accuracy and false discovery rate were used to evaluate the performance of artifact detection approaches. Criminisi algorithm was used for image restoration with peak signal-to-noise ratio (PSNR) and mean gradient difference to evaluate its performance. In addition, computation complexity was evaluated by execution time of relevant algorithms.
Results:
Results revealed that the proposed joint approach with edge-connection and Criminisi algorithm could achieve automatic artifacts removal. Mean detection accuracy and mean false discovery rate of the proposed edge-connection algorithm for the 50 ultrasound images were 0.86 and 1.50. Mean PSNR of the 50 restored images by Criminisi algorithm was 36.64 dB, and mean gradient difference of the restored images was -0.002 compared with the original images.
Conclusions:
The proposed combined approach had a good detection accuracy for different types of manually induced artifacts, and could significantly improve PSNR of the ultrasound images. The proposed combined approach may have potential use for the repair of ultrasound images with artifacts.
