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Med. Biol."],"published-print":{"date-parts":[[2025,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Ultrasound is the predominant modality in medical practice for evaluating thyroid nodules. Currently, diagnosis is typically based on textural information. This study aims to develop an automated texture classification approach to aid physicians in interpreting ultrasound images of thyroid nodules. However, there is currently a scarcity of pixel-level labeled datasets for the texture classes of thyroid nodules. The labeling of such datasets relies on professional and experienced doctors, requiring a significant amount of manpower. Therefore, the objective of this study is to develop an unsupervised method for classifying nodule textures. <jats:italic>Approach.<\/jats:italic> Firstly, we develop a spatial mapping network to transform the one-dimensional pixel value space into a high-dimensional space to extract comprehensive feature information. Subsequently, we outline the principles of feature selection that are suitable for clustering. Then we propose a pixel-level clustering algorithm with a region growth pattern, and a distance evaluation method for texture sets among different nodules is established. <jats:italic>Main results.<\/jats:italic> Our algorithm achieves a pixel-level classification accuracy of 0.931 for the cystic and solid region, 0.870 for the hypoechoic region, 0.959 for the isoechoic region, and 0.961 for the hyperechoic region. The efficacy of our algorithm and its concordance with human observation have been demonstrated. Furthermore, we conduct calculations and visualize the distribution of different textures in benign and malignant nodules. <jats:italic>Significance.<\/jats:italic> This method can be used for the automatic generation of pixel-level labels of thyroid nodule texture, aiding in the construction of texture datasets, and offering image analysis information for medical professionals.<\/jats:p>","DOI":"10.1088\/1361-6560\/ada5a6","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T22:55:30Z","timestamp":1735944930000},"page":"025025","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["An unsupervised automatic texture classification method for ultrasound images of thyroid nodules"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5648-1526","authenticated-orcid":true,"given":"Chenzhuo","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8825-4029","authenticated-orcid":true,"given":"Zhuang","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Fei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5266-383X","authenticated-orcid":true,"given":"Rongli","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyue","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,1,21]]},"reference":[{"key":"pmbada5a6bib1","doi-asserted-by":"publisher","first-page":"2527","DOI":"10.1109\/TCSVT.2021.3074414","article-title":"Multimodal feature fusion and knowledge-driven learning via experts consult for thyroid nodule classification","volume":"32","author":"Avola","year":"2021","journal-title":"IEEE Trans. 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