| dc.contributor | 圖檔所 | - |
| dc.creator (作者) | 羅崇銘 | - |
| dc.creator (作者) | Lo*, Chung-Ming | - |
| dc.creator (作者) | Hsieh, Kevin Li-Chun | - |
| dc.creator (作者) | Chen, Cheng-Yu | - |
| dc.date (日期) | 2017-04 | - |
| dc.date.accessioned | 19-Sep-2019 09:54:47 (UTC+8) | - |
| dc.date.available | 19-Sep-2019 09:54:47 (UTC+8) | - |
| dc.date.issued (上傳時間) | 19-Sep-2019 09:54:47 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/126341 | - |
| dc.description.abstract (摘要) | BACKGROUND: A 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. METHOD: In 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. RESULTS: The 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. CONCLUSIONS: More-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. | - |
| dc.relation (關聯) | Computers in Biology and Medicine, Vol.83, pp.102-108 | - |
| dc.subject (關鍵詞) | rain tumor; Computer-aided diagnosis; Glioma; Local binary pattern; Magnetic resonance imaging | - |
| dc.title (題名) | Quantitative glioma grading using transformed gray-scale invariant textures of MRI | - |
| dc.type (資料類型) | article | - |
| dc.identifier.doi (DOI) | 10.1016/j.compbiomed.2017.02.012 | - |
| dc.doi.uri (DOI) | https://doi.org/10.1016/j.compbiomed.2017.02.012 | - |
