Academic Journal

Development of a Stand-Alone Independent Graphical User Interface for Neurological Disease Prediction with Automated Extraction and Segmentation of Gray and White Matter in Brain MRI Images

التفاصيل البيبلوغرافية
العنوان: Development of a Stand-Alone Independent Graphical User Interface for Neurological Disease Prediction with Automated Extraction and Segmentation of Gray and White Matter in Brain MRI Images
المؤلفون: Ayush Goyal, Sunayana Tirumalasetty, Gahangir Hossain, Rajab Challoo, Manish Arya, Rajeev Agrawal, Deepak Agrawal
المصدر: Journal of Healthcare Engineering, Vol 2019 (2019)
بيانات النشر: Hindawi Limited
سنة النشر: 2019
المجموعة: Directory of Open Access Journals: DOAJ Articles
مصطلحات موضوعية: Medicine (General), R5-920, Medical technology, R855-855.5
الوصف: This research presents an independent stand-alone graphical computational tool which functions as a neurological disease prediction framework for diagnosis of neurological disorders to assist neurologists or researchers in the field to perform automatic segmentation of gray and white matter regions in brain MRI images. The tool was built in collaboration with neurologists and neurosurgeons and many of the features are based on their feedback. This tool provides the user automatized functionality to perform automatic segmentation and extract the gray and white matter regions of patient brain image data using an algorithm called adapted fuzzy c-means (FCM) membership-based clustering with preprocessing using the elliptical Hough transform and postprocessing using connected region analysis. Dice coefficients for several patient brain MRI images were calculated to measure the similarity between the manual tracings by experts and automatic segmentations obtained in this research. The average Dice coefficients are 0.86 for gray matter, 0.88 for white matter, and 0.87 for total cortical matter. Dice coefficients of the proposed algorithm were also the highest when compared with previously published standard state-of-the-art brain MRI segmentation algorithms in terms of accuracy in segmenting the gray matter, white matter, and total cortical matter.
نوع الوثيقة: article in journal/newspaper
اللغة: English
تدمد: 2040-2295
2040-2309
Relation: http://dx.doi.org/10.1155/2019/9610212; https://doaj.org/toc/2040-2295; https://doaj.org/toc/2040-2309; https://doaj.org/article/d70d67d5ac57463c9b5e442be1754eb6
DOI: 10.1155/2019/9610212
الاتاحة: https://doi.org/10.1155/2019/9610212
https://doaj.org/article/d70d67d5ac57463c9b5e442be1754eb6
رقم الانضمام: edsbas.BFBC4BB5
قاعدة البيانات: BASE
الوصف
تدمد:20402295
20402309
DOI:10.1155/2019/9610212