Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior
المؤلفون المشاركون
Yang, Wei
Feng, Qianjin
Lu, Yisu
Jiang, Jun
Chen, Wufan
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-08-31
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning.
It is well-known that the number of clusters is one of the most important parameters for automatic segmentation.
However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions.
In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters.
Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study.
Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time.
The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches.
The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Lu, Yisu& Jiang, Jun& Yang, Wei& Feng, Qianjin& Chen, Wufan. 2014. Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034668
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Lu, Yisu…[et al.]. Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1034668
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Lu, Yisu& Jiang, Jun& Yang, Wei& Feng, Qianjin& Chen, Wufan. Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034668
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1034668
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