کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6920353 | 864276 | 2013 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Level set method with automatic selective local statistics for brain tumor segmentation in MR images
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Level set method with automatic selective local statistics for brain tumor segmentation in MR images Level set method with automatic selective local statistics for brain tumor segmentation in MR images](/preview/png/6920353.png)
چکیده انگلیسی
The level set approach is a powerful tool for segmenting images. This paper proposes a method for segmenting brain tumor images from MR images. A new signed pressure function (SPF) that can efficiently stop the contours at weak or blurred edges is introduced. The local statistics of the different objects present in the MR images were calculated. Using local statistics, the tumor objects were identified among different objects. In this level set method, the calculation of the parameters is a challenging task. The calculations of different parameters for different types of images were automatic. The basic thresholding value was updated and adjusted automatically for different MR images. This thresholding value was used to calculate the different parameters in the proposed algorithm. The proposed algorithm was tested on the magnetic resonance images of the brain for tumor segmentation and its performance was evaluated visually and quantitatively. Numerical experiments on some brain tumor images highlighted the efficiency and robustness of this method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computerized Medical Imaging and Graphics - Volume 37, Issues 7â8, OctoberâDecember 2013, Pages 522-537
Journal: Computerized Medical Imaging and Graphics - Volume 37, Issues 7â8, OctoberâDecember 2013, Pages 522-537
نویسندگان
Kiran Thapaliya, Jae-Young Pyun, Chun-Su Park, Goo-Rak Kwon,