ArticleJournal of medical systems2019
Segmentation and Diagnosis of Papillary Thyroid Carcinomas Based on Generalized Clustering Algorithm in Ultrasound Elastography.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- EvoThy-Net: an evolutionary encoder-decoder network for thyroid nodule segmentation in ultrasound imaging.Scientific reports · 2026Article
- Modulation of Intestinal Flora: a Novel Immunotherapeutic Approach for Enhancing Thyroid Cancer Treatment.Probiotics and antimicrobial proteins · 2025Review
- Predictive value of an ultrasound-based radiomics model for central lymph node metastasis of papillary thyroid carcinoma.International journal of medical sciences · 2024Article
- Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review.Frontiers in oncology · 2023Review
- Visual Interpretability in Computer-Assisted Diagnosis of Thyroid Nodules Using Ultrasound Images.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
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1 author.
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Abstract
Papillary thyroid carcinomas (PTC) are the most common type of thyroid malignant tumors. Existing methods for clustering high-noise ultrasound images tend to degrade the clustering performance. In order to realize accurate segmentation of thyroid nodule in noisy environment, this paper proposes an improved segmentation algorithm based on adaptive fast generalized clustering. Firstly, the parameter balance factor is adaptively determined according to the noise probability of non-local pixels so as to reflect the spatial structure information in the image more accurately. Then, the balance factor is used to effectively combine the linear weighted filtered image in the AFGC algorithm so as to create the adaptive filtered image. Since the filtering degree depends on the probability whether the pixel is noise in the image, the dynamic noise suppression performance of the proposed method can be greatly improved. A large number of qualitative and quantitative experimental results show that the proposed generalized clustering algorithm can obtain more accurate results when clustering images with high noise. It is suitable for intelligent diagnosis of papillary thyroid convolution in clinical examination.
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