3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models
المؤلفون المشاركون
Khalifa, Fahmi
Soliman, Ahmed
Elmaghraby, Adel
El-Baz, Ayman
Gimel'farb, Georgy
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-02-09
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment.
This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach.
To account for CT images’ inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance.
To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family.
The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels’ appearances in neighboring spatial voxel locations.
Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration.
Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Khalifa, Fahmi& Soliman, Ahmed& Elmaghraby, Adel& Gimel'farb, Georgy& El-Baz, Ayman. 2017. 3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142439
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Khalifa, Fahmi…[et al.]. 3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1142439
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Khalifa, Fahmi& Soliman, Ahmed& Elmaghraby, Adel& Gimel'farb, Georgy& El-Baz, Ayman. 3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142439
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142439
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