Computer-aided diagnosis of ischemic stroke using multi-dimensional image features in carotid color Doppler

被引:19
作者
Lo, Chung-Ming [1 ]
Hung, Peng-Hsiang [2 ,3 ]
机构
[1] Natl Chengchi Univ, Grad Inst Lib Informat & Archival Studies, Taipei, Taiwan
[2] Mackay Mem Hosp, Dept Radiol, Taipei, Taiwan
[3] Mackay Mem Hosp, Dept Radiol, 92, Sec 2,Zhongshan N Rd, Taipei 10449, Taiwan
关键词
PLAQUE MORPHOLOGY; MYOCARDIAL-INFARCTION; PATTERN-RECOGNITION; ARTERY-DISEASE; STENOSIS; CLASSIFICATION; ENDARTERECTOMY; IDENTIFICATION; ANGIOGRAPHY; CRITERIA;
D O I
10.1016/j.compbiomed.2022.105779
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
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.
引用
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页数:10
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共 44 条
[41]   Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features [J].
Agliozzo, S. ;
De Luca, M. ;
Bracco, C. ;
Vignati, A. ;
Giannini, V. ;
Martincich, L. ;
Carbonaro, L. A. ;
Bert, A. ;
Sardanelli, F. ;
Regge, D. .
MEDICAL PHYSICS, 2012, 39 (04) :1704-1715
[42]   Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement [J].
Newell, Dustin ;
Nie, Ke ;
Chen, Jeon-Hor ;
Hsu, Chieh-Chih ;
Yu, Hon J. ;
Nalcioglu, Orhan ;
Su, Min-Ying .
EUROPEAN RADIOLOGY, 2010, 20 (04) :771-781
[43]   Computer-aided diagnosis of Parkinson's disease based on [123I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines [J].
Oliveira, Francisco P. M. ;
Castelo-Branco, Miguel .
JOURNAL OF NEURAL ENGINEERING, 2015, 12 (02)
[44]   BUS-CAD: A computer-aided diagnosis system for breast tumor classification in ultrasound images using grid-search-optimized machine learning algorithms with extended and Boruta-selected features [J].
Ozcan, Hakan .
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2023, 33 (05) :1480-1493