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https://ah.lib.nccu.edu.tw/handle/140.119/126341
題名: | Quantitative glioma grading using transformed gray-scale invariant textures of MRI | 作者: | 羅崇銘 Lo*, Chung-Ming Hsieh, Kevin Li-Chun Chen, Cheng-Yu |
貢獻者: | 圖檔所 | 關鍵詞: | rain tumor; Computer-aided diagnosis; Glioma; Local binary pattern; Magnetic resonance imaging | 日期: | 四月-2017 | 上傳時間: | 19-九月-2019 | 摘要: | BACKGROUND:\r\nA computer-aided diagnosis (CAD) system based on intensity-invariant magnetic resonance (MR) imaging features was proposed to grade gliomas for general application to various scanning systems and settings.\r\n\r\nMETHOD:\r\nIn total, 34 glioblastomas and 73 lower-grade gliomas comprised the image database to evaluate the proposed CAD system. For each case, the local texture on MR images was transformed into a local binary pattern (LBP) which was intensity-invariant. From the LBP, quantitative image features, including the histogram moment and textures, were extracted and combined in a logistic regression classifier to establish a malignancy prediction model. The performance was compared to conventional texture features to demonstrate the improvement.\r\n\r\nRESULTS:\r\nThe performance of the CAD system based on LBP features achieved an accuracy of 93% (100/107), a sensitivity of 97% (33/34), a negative predictive value of 99% (67/68), and an area under the receiver operating characteristic curve (Az) of 0.94, which were significantly better than the conventional texture features: an accuracy of 84% (90/107), a sensitivity of 76% (26/34), a negative predictive value of 89% (64/72), and an Az of 0.89 with respective p values of 0.0303, 0.0122, 0.0201, and 0.0334.\r\n\r\nCONCLUSIONS:\r\nMore-robust texture features were extracted from MR images and combined into a significantly better CAD system for distinguishing glioblastomas from lower-grade gliomas. The proposed CAD system would be more practical in clinical use with various imaging systems and settings. | 關聯: | Computers in Biology and Medicine, Vol.83, pp.102-108 | 資料類型: | article | DOI: | https://doi.org/10.1016/j.compbiomed.2017.02.012 |
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