دورية أكاديمية
Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images.
| العنوان: | Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images. |
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| المؤلفون: | Wang YY; Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China., Peng WX; Radiology Department of Hangzhou Medical College, China., Qiu CH; Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China., Jiang J; Second Affiliated Hospital, Zhejiang University School of Medicine, China., Xia SR; Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China. Electronic address: corresponding_xia@163.com. |
| المصدر: | Ultrasonics [Ultrasonics] 2019 Feb; Vol. 92, pp. 1-7. Date of Electronic Publication: 2018 Jun 18. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Science Country of Publication: Netherlands NLM ID: 0050452 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1874-9968 (Electronic) Linking ISSN: 0041624X NLM ISO Abbreviation: Ultrasonics Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Publication: 1995- : Amsterdam : Elsevier Science Original Publication: London. Butterworth Scientific Ltd., |
| مستخلص: | Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature. (Copyright © 2018. Published by Elsevier B.V.) |
| فهرسة مساهمة: | Keywords: Border detection; Feature selection; Fractional calculus; Intravascular ultrasound image; Multi-classification |
| تواريخ الأحداث: | Date Created: 20180912 Date Completed: 20181023 Latest Revision: 20181023 |
| رمز التحديث: | 20240829 |
| DOI: | 10.1016/j.ultras.2018.06.012 |
| PMID: | 30205179 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1874-9968 |
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| DOI: | 10.1016/j.ultras.2018.06.012 |