Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing
المؤلفون المشاركون
Li, Bailin
Zhou, Zhaozhong
Ding, Xiaokang
Deng, Xiaolei
Zou, Ling
Jiang, Xiaoliang
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-01-15
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
The hippocampus has been known as one of the most important structures referred to as Alzheimer’s disease and other neurological disorders.
However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries.
For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed.
Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method.
Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation.
Finally, gradient descent method is adopted to get the minimization of the energy equation.
As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Jiang, Xiaoliang& Zhou, Zhaozhong& Ding, Xiaokang& Deng, Xiaolei& Zou, Ling& Li, Bailin. 2017. Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1142173
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Jiang, Xiaoliang…[et al.]. Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1142173
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Jiang, Xiaoliang& Zhou, Zhaozhong& Ding, Xiaokang& Deng, Xiaolei& Zou, Ling& Li, Bailin. Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1142173
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142173
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