基于C-V模型的脑白质疏松症磁共振图像病变区域分割

针对脑白质疏松症病变区域在磁共振图像的T₂加权像上呈现斑块状或融合成片状的高亮信号这一特点,提出了一种基于C-V模型的水平集分割方法对病变区域进行图像分割。首先,对C-V模型进行改进以避免重新初始化问题;然后,使用Otsu阈值法对图像进行预分割,将预分割的结果直接作为改进C-V模型的初始轮廓;最后,利用水平集方法进行曲线演化,得到最终的分割轮廓。实验结果表明,该方法能较为准确地分割出病变区域,实现病变区域的计算机自动快速分割,对脑白质疏松症临床辅助诊断和预后判断有一定的应用价值。

Lesion area segmentation in leukoaraiosis's magnetic resonance image based on C-V model

Concerning that the lesion areas of leukoaraiosis in Magnetic Resonance (MR) image present hyper intense signal on T₂ flair sequence, a level set segmentation method based on C-V model was proposed. First, the C-V model was improved to avoid the re-initialization; second, the Otsu threshold method was used for image's pre-segmentation, and then the image's pre-segmentation result was directly used as the initial contour for the improved C-V model; finally, the segmentation result was obtained by curve evolution. The results show that the proposed segmentation method can get better separation effects, and realize fast auto-segmentation. It has certain application value for clinical diagnosis and prognosis on leukoaraiosis.