Department of Information and Communication Engineering, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 501-759, South Korea.
Comput Med Imaging Graph. 2013 Oct-Dec;37(7-8):522-37. doi: 10.1016/j.compmedimag.2013.05.003. Epub 2013 Oct 20.
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.
水平集方法是分割图像的有力工具。本文提出了一种从磁共振图像中分割脑肿瘤图像的方法。引入了一种新的有符号压力函数（SPF），它可以有效地在弱边缘或模糊边缘停止轮廓。计算了磁共振图像中不同物体的局部统计信息。使用局部统计信息，在不同物体中识别出肿瘤物体。在这种水平集方法中，参数的计算是一项具有挑战性的任务。不同类型图像的参数计算是自动的。基本的阈值被自动更新和调整，以适应不同的磁共振图像。这个阈值被用来计算所提出算法中的不同参数。所提出的算法在脑肿瘤磁共振图像上进行了分割，并从视觉和定量两个方面对其性能进行了评估。对一些脑肿瘤图像的数值实验突出了这种方法的效率和鲁棒性。
