Segmentation of low-grade gliomas based on the growing region and level sets techniques

R. Zaouche, Ahror Belaid, Bassel Solaiman, D. Salem, S. Tliba
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引用次数: 2

Abstract

In this paper, we propose a novel semi-automatic segmentation method based on the local image properties. Its originality is twofold, the first stands on the intensity invariant of phase-local information for the purpose of low-grade gliomas segmentation in MR images. In a second time, a level set method driven is combined to growing region so as to improve tumor detection. Experiments were conducted on a set of medical images. A comparison between the obtained results and the manual segmentation collected from experts is performed. The preliminary results are interesting and encouraging.
基于生长区域和水平集技术的低级别胶质瘤分割
本文提出了一种基于局部图像属性的半自动图像分割方法。其独创性体现在两个方面,一是立足于相位局部信息的强度不变性,用于MR图像中低级别胶质瘤的分割。第二次将水平集驱动方法与生长区域相结合,提高了肿瘤的检测能力。在一组医学图像上进行了实验。将得到的结果与从专家那里收集的人工分割进行比较。初步结果是有趣和令人鼓舞的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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