Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT
المؤلفون المشاركون
Qadeer, Nouman
Liu, Xiabi
Han, Guanghui
Zhao, Yanfeng
Zhao, Xinming
Zhou, Chunwu
Sun, Jia
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-03-29
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Computer-aided detection (CAD) of lobulation can help radiologists to diagnose/detect lung diseases easily and accurately.
Compared to CAD of nodule and other lung lesions, CAD of lobulation remained an unexplored problem due to very complex and varying nature of lobulation.
Thus, many state-of-the-art methods could not detect successfully.
Hence, we revisited classical methods with the capability of extracting undulated characteristics and designed a sliding window based framework for lobulation detection in this paper.
Under the designed framework, we investigated three categories of lobulation classification algorithms: template matching, feature based classifier, and bending energy.
The resultant detection algorithms were evaluated through experiments on LISS database.
The experimental results show that the algorithm based on combination of global context feature and BOF encoding has best overall performance, resulting in F1 score of 0.1009.
Furthermore, bending energy method is shown to be appropriate for reducing false positives.
We performed bending energy method following the LIOP-LBP mixture feature, the average positive detection per image was reduced from 30 to 22, and F1 score increased to 0.0643 from 0.0599.
To the best of our knowledge this is the first kind of work for direct lobulation detection and first application of bending energy to any kind of lobulation work.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Han, Guanghui& Liu, Xiabi& Qadeer, Nouman& Sun, Jia& Zhao, Yanfeng& Zhao, Xinming…[et al.]. 2017. Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT. BioMed Research International،Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1136446
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Han, Guanghui…[et al.]. Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT. BioMed Research International No. 2017 (2017), pp.1-15.
https://search.emarefa.net/detail/BIM-1136446
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Han, Guanghui& Liu, Xiabi& Qadeer, Nouman& Sun, Jia& Zhao, Yanfeng& Zhao, Xinming…[et al.]. Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1136446
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1136446
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