【摘要】 由于医学图像通常伴有灰度不均、背景复杂的特点,传统水平集无法对其进行有效分割,因此提出了一种多分辨率改进型双水平集算法。首先,利用小波进行多尺度空间分析,从而获取医学图像的粗尺度图像;然后由改进型双水平集对图像进行分割,提取多目标区域;为了去除医学图像中灰度不均对分割效果的影响,该算法引入偏移场拟合项,以进一步改进双水平集模型,进而对粗尺度分割效果进行优化处理。实验结果表明,所提算法能有效地解决灰度不均与背景复杂的问题,将伴灰度不均的多目标医学图像完全分割出来,从而获得预期的分割效果。更多还原

【Abstract】 This paper proposed a novel multiresolution double level set algorithm for medical image,which has a large amount of intensity inhomogeneities and complicated background,and can not be separated completely by traditional level set.First of all,the algorithm gets the coarse scale image by analyzing the image with wavelet multiscale decomposition.Then,the algorithm identifies multiple targets by segmenting the analysed results in terms of improved double level set model.In order to deal with the effect of intensity inhomogeneities on the medical image,the algorithm introduces a bias fitting term into the improved double level set model and optimizes the coarse-scale segmentation result.The experimental result shows that the algorithm can reduce the problems of intensity inhomogeneities and complicated background,separate medical image including intensity inhomogeneities and multiple objects completely,and obtain the expected effect of segmentation.更多还原