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Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/126341
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| Title: | Quantitative glioma grading using transformed gray-scale invariant textures of MRI |
| Authors: | 羅崇銘
Lo*, Chung-Ming
Hsieh, Kevin Li-Chun
Chen, Cheng-Yu |
| Contributors: | 圖檔所 |
| Keywords: | rain tumor;Computer-aided diagnosis;Glioma;Local binary pattern;Magnetic resonance imaging |
| Date: | 2017-04 |
| Issue Date: | 2019-09-19 09:54:47 (UTC+8) |
| Abstract: | 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. |
| Relation: | Computers in Biology and Medicine, Vol.83, pp.102-108 |
| Data Type: | article |
| DOI 連結: | https://doi.org/10.1016/j.compbiomed.2017.02.012 |
| DOI: | 10.1016/j.compbiomed.2017.02.012 |
| Appears in Collections: | [圖書資訊與檔案學研究所] 期刊論文
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