Brain Medical Image Fusion Based on Dual-Branch CNNs in NSST Domain
المؤلفون المشاركون
Zhou, Dongming
Nie, Rencan
Ding, Zhaisheng
Hou, Ruichao
Liu, Yanyu
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-04-14
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Computed tomography (CT) images show structural features, while magnetic resonance imaging (MRI) images represent brain tissue anatomy but do not contain any functional information.
How to effectively combine the images of the two modes has become a research challenge.
In this paper, a new framework for medical image fusion is proposed which combines convolutional neural networks (CNNs) and non-subsampled shearlet transform (NSST) to simultaneously cover the advantages of them both.
This method effectively retains the functional information of the CT image and reduces the loss of brain structure information and spatial distortion of the MRI image.
In our fusion framework, the initial weights integrate the pixel activity information from two source images that is generated by a dual-branch convolutional network and is decomposed by NSST.
Firstly, the NSST is performed on the source images and the initial weights to obtain their low-frequency and high-frequency coefficients.
Then, the first component of the low-frequency coefficients is fused by a novel fusion strategy, which simultaneously copes with two key issues in the fusion processing which are named energy conservation and detail extraction.
The second component of the low-frequency coefficients is fused by the strategy that is designed according to the spatial frequency of the weight map.
Moreover, the high-frequency coefficients are fused by the high-frequency components of the initial weight.
Finally, the final image is reconstructed by the inverse NSST.
The effectiveness of the proposed method is verified using pairs of multimodality images, and the sufficient experiments indicate that our method performs well especially for medical image fusion.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ding, Zhaisheng& Zhou, Dongming& Nie, Rencan& Hou, Ruichao& Liu, Yanyu. 2020. Brain Medical Image Fusion Based on Dual-Branch CNNs in NSST Domain. BioMed Research International،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1135690
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ding, Zhaisheng…[et al.]. Brain Medical Image Fusion Based on Dual-Branch CNNs in NSST Domain. BioMed Research International No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1135690
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ding, Zhaisheng& Zhou, Dongming& Nie, Rencan& Hou, Ruichao& Liu, Yanyu. Brain Medical Image Fusion Based on Dual-Branch CNNs in NSST Domain. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1135690
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1135690
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