Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering
المؤلفون المشاركون
Gallegos, F. J.
Mújica Vargas, Dante
Vianney Kinani, Jean Marie
Rosales Silva, Alberto Jorge
Ramos Díaz, Eduardo
Arellano, Alfonso
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-10-12
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy.
We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering.
We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation.
The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy.
Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method.
An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained.
Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets.
As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Vianney Kinani, Jean Marie& Rosales Silva, Alberto Jorge& Gallegos, F. J.& Mújica Vargas, Dante& Ramos Díaz, Eduardo& Arellano, Alfonso. 2017. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1181256
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Vianney Kinani, Jean Marie…[et al.]. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering. Journal of Healthcare Engineering No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1181256
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Vianney Kinani, Jean Marie& Rosales Silva, Alberto Jorge& Gallegos, F. J.& Mújica Vargas, Dante& Ramos Díaz, Eduardo& Arellano, Alfonso. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1181256
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1181256
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