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生物医学工程评论综述™

每年出版 6 

ISSN 打印: 0278-940X

ISSN 在线: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

Indexed in

Review of Texture Quantification of CT Images for Classification of Lung Diseases

卷 43, 册 2-3, 2015, pp. 183-200
DOI: 10.1615/CritRevBiomedEng.2015011026
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摘要

Computer-based identification of abnormal regions and classification of diseases using CT images of the lung has been a goal of many investigators. In this paper, we review research that has used texture analysis along with segmentation and fractal analysis. First, a review of texture methods is performed. Recent research on quantitative analysis of the lung using texture methods is categorized into six groups of computational methods: structural, statistical, model based, transform domain, texture-segmentation, and texture-fractal analysis. Finally, the applications of texture-based methods combined with either segmentation algorithms or fractal analysis is evaluated on lung CT images from patients with diseases such as emphysema, COPD, and cancer. We also discuss applications of artificial neural networks, support vector machine, k-nearest, and Bayesian methods to classify normal and diseased segments of CT images of the lung. A combination of these texture methods followed by classifiers could lead to efficient and accurate diagnosis of pulmonary diseases such as pulmonary fibrosis, emphysema, and cancer.

对本文的引用
  1. Phillips Iain, Ajaz Mazhar, Ezhil Veni, Prakash Vineet, Alobaidli Sheaka, McQuaid Sarah J, South Christopher, Scuffham James, Nisbet Andrew, Evans Philip, Clinical applications of textural analysis in non-small cell lung cancer, The British Journal of Radiology, 91, 1081, 2018. Crossref

  2. Lubner Meghan G., Smith Andrew D., Sandrasegaran Kumar, Sahani Dushyant V., Pickhardt Perry J., CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges, RadioGraphics, 37, 5, 2017. Crossref

  3. Soo E., Adamali H., Edey A.J., Idiopathic pulmonary fibrosis: current and future directions, Clinical Radiology, 72, 5, 2017. Crossref

  4. Capaccione Kathleen M., Wang Aijin, Lee Shing M., Patel Nina, Austin John H.M., Maino Pamela, Padilla Maria, Salvatore Mary M., Quantifying normal lung in pulmonary fibrosis: CT analysis and correlation with %DLCO, Clinical Imaging, 77, 2021. Crossref

  5. Kloth Christopher, Blum Anya C., Thaiss Wolfgang M., Preibsch Heike, Ditt Hendrik, Grimmer Rainer, Fritz Jan, Nikolaou Konstantin, Bösmüller Hans, Horger Marius, Differences in Texture Analysis Parameters Between Active Alveolitis and Lung Fibrosis in Chest CT of Patients with Systemic Sclerosis, Academic Radiology, 24, 12, 2017. Crossref

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