Publications-Periodical Articles
Article View/Open
Publication Export
Google ScholarTM
NCCU Library
Citation Infomation
-
Loading...Loading...
Related Publications in TAIR
| Title | Computer-aided Diagnosis of Ischemic Stroke using Multi-dimensional Image Features in Carotid Color Doppler |
| Creator | 羅崇銘 Lo, Chung-Ming Hung, Peng-Hsiang |
| Contributor | 圖檔所 |
| Date | 2022-08 |
| Date Issued | 2-Dec-2022 15:34:17 (UTC+8) |
| Summary | Purpose Stroke is one of the leading causes of disability and mortality. Carotid atherosclerosis is a crucial factor in the occurrence of ischemic stroke. To achieve timely recognition, a computer-aided diagnosis (CAD) system was proposed to evaluate the ischemic stroke patterns in carotid color Doppler (CCD). Methods A total of 513 stroke and 458 normal CCD images were collected from 102 stroke and 75 normal patients, respectively. For each image, quantitative histogram, shape, and texture features were extracted to interpret the diagnostic information. In the experiment, a logistic regression classifier with backward elimination and leave-one-out cross validation was used to combine features as a prediction model. Results The performance of the CAD system using histogram, shape, and texture features achieved accuracies of 87%, 60%, and 87%, respectively. With respect to the combined features, the CAD achieved an accuracy of 89%, a sensitivity of 89%, a specificity of 88%, a positive predictive value of 89%, a negative predictive value of 88%, and Kappa = 0.77, with an area under the receiver operating characteristic curve of 0.94. Conclusions Based on the extracted quantitative features in the CCD images, the proposed CAD system provides valuable suggestions for assisting physicians in improving ischemic stroke diagnoses during carotid ultrasound examination. |
| Relation | Computers in Biology and Medicine, Vol.147, 105779 |
| Type | article |
| DOI | https://doi.org/10.1016/j.compbiomed.2022.105779 |
| dc.contributor | 圖檔所 | |
| dc.creator (作者) | 羅崇銘 | |
| dc.creator (作者) | Lo, Chung-Ming | |
| dc.creator (作者) | Hung, Peng-Hsiang | |
| dc.date (日期) | 2022-08 | |
| dc.date.accessioned | 2-Dec-2022 15:34:17 (UTC+8) | - |
| dc.date.available | 2-Dec-2022 15:34:17 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Dec-2022 15:34:17 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/142697 | - |
| dc.description.abstract (摘要) | Purpose Stroke is one of the leading causes of disability and mortality. Carotid atherosclerosis is a crucial factor in the occurrence of ischemic stroke. To achieve timely recognition, a computer-aided diagnosis (CAD) system was proposed to evaluate the ischemic stroke patterns in carotid color Doppler (CCD). Methods A total of 513 stroke and 458 normal CCD images were collected from 102 stroke and 75 normal patients, respectively. For each image, quantitative histogram, shape, and texture features were extracted to interpret the diagnostic information. In the experiment, a logistic regression classifier with backward elimination and leave-one-out cross validation was used to combine features as a prediction model. Results The performance of the CAD system using histogram, shape, and texture features achieved accuracies of 87%, 60%, and 87%, respectively. With respect to the combined features, the CAD achieved an accuracy of 89%, a sensitivity of 89%, a specificity of 88%, a positive predictive value of 89%, a negative predictive value of 88%, and Kappa = 0.77, with an area under the receiver operating characteristic curve of 0.94. Conclusions Based on the extracted quantitative features in the CCD images, the proposed CAD system provides valuable suggestions for assisting physicians in improving ischemic stroke diagnoses during carotid ultrasound examination. | |
| dc.format.extent | 112 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Computers in Biology and Medicine, Vol.147, 105779 | |
| dc.title (題名) | Computer-aided Diagnosis of Ischemic Stroke using Multi-dimensional Image Features in Carotid Color Doppler | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1016/j.compbiomed.2022.105779 | |
| dc.doi.uri (DOI) | https://doi.org/10.1016/j.compbiomed.2022.105779 |