A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction
المؤلفون المشاركون
Zhao, Dazhe
Li, Wei
Yu, Kun
Feng, Chaolu
Yang, Jinzhu
Lou, Chunhui
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-06-01
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص EN
Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities.
A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions.
First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model.
Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively.
Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively.
Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Feng, Chaolu& Yang, Jinzhu& Lou, Chunhui& Li, Wei& Yu, Kun& Zhao, Dazhe. 2020. A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1139576
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Feng, Chaolu…[et al.]. A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1139576
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Feng, Chaolu& Yang, Jinzhu& Lou, Chunhui& Li, Wei& Yu, Kun& Zhao, Dazhe. A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1139576
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139576
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