Unsupervised Scoliosis Diagnosis via a Joint Recognition Method with Multifeature Descriptors and Centroids Extraction
المؤلفون المشاركون
Zhang, Liyuan
Zhao, Jiashi
Yang, Huamin
Jiang, Zhengang
Li, Qingliang
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-09-25
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
To solve the problem of scoliosis recognition without a labeled dataset, an unsupervised method is proposed by combining the cascade gentle AdaBoost (CGAdaBoost) classifier and distance regularized level set evolution (DRLSE).
The main idea of the proposed method is to establish the relationship between individual vertebrae and the whole spine with vertebral centroids.
Scoliosis recognition can be transferred into automatic vertebral detection and segmentation processes, which can avoid the manual data-labeling processing.
In the CGAdaBoost classifier, diversified vertebrae images and multifeature descriptors are considered to generate more discriminative features, thus improving the vertebral detection accuracy.
After that, the detected bounding box represents an appropriate initial contour of DRLSE to make the vertebral segmentation more accurate.
It is helpful for the elimination of initialization sensitivity and quick convergence of vertebra boundaries.
Meanwhile, vertebral centroids are extracted to connect the whole spine, thereby describing the spinal curvature.
Different parts of the spine are determined as abnormal or normal in accordance with medical prior knowledge.
The experimental results demonstrate that the proposed method cannot only effectively identify scoliosis with unlabeled spine CT images but also have superiority against other state-of-the-art methods.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhang, Liyuan& Zhao, Jiashi& Yang, Huamin& Jiang, Zhengang& Li, Qingliang. 2018. Unsupervised Scoliosis Diagnosis via a Joint Recognition Method with Multifeature Descriptors and Centroids Extraction. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1132079
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhang, Liyuan…[et al.]. Unsupervised Scoliosis Diagnosis via a Joint Recognition Method with Multifeature Descriptors and Centroids Extraction. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1132079
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhang, Liyuan& Zhao, Jiashi& Yang, Huamin& Jiang, Zhengang& Li, Qingliang. Unsupervised Scoliosis Diagnosis via a Joint Recognition Method with Multifeature Descriptors and Centroids Extraction. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1132079
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1132079
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر