کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6920351 | 864276 | 2013 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a method to construct a graph by learning the population- and patient-specific feature sets of multimodal magnetic resonance (MR) images and by utilizing the graph-cut to achieve a final segmentation. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through learning population- and patient-specific feature sets, respectively. The proposed method is evaluated using 23 glioma image sequences, and the segmentation results are compared with other approaches. The encouraging evaluation results obtained, i.e., DSC (84.5%), Jaccard (74.1%), sensitivity (87.2%), and specificity (83.1%), show that the proposed method can effectively make use of both population- and patient-specific information.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computerized Medical Imaging and Graphics - Volume 37, Issues 7â8, OctoberâDecember 2013, Pages 512-521
Journal: Computerized Medical Imaging and Graphics - Volume 37, Issues 7â8, OctoberâDecember 2013, Pages 512-521
نویسندگان
Jun Jiang, Yao Wu, Meiyan Huang, Wei Yang, Wufan Chen, Qianjin Feng,