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題名 Computer-aided Diagnosis of Ischemic Stroke using Multi-dimensional Image Features in Carotid Color Doppler 作者 羅崇銘
Lo, Chung-Ming
Hung, Peng-Hsiang貢獻者 圖檔所 日期 2022-08 上傳時間 2-Dec-2022 15:34:17 (UTC+8) 摘要 PurposeStroke 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).MethodsA 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.ResultsThe 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.ConclusionsBased 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. 關聯 Computers in Biology and Medicine, Vol.147, 105779 資料類型 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) PurposeStroke 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).MethodsA 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.ResultsThe 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.ConclusionsBased 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
