基于混合分水岭变换和模糊C均值的图像分割算法

Hybrid Method of Image Segmentation Based on Watershed Transform and Fuzzy C-means

分水岭变换是图像分割的一种强有力的形态工具,能够自动生成一系列封闭分割区域。其不足之处是过分割、对噪声敏感。为克服分水岭变换固有的缺点,本文综合利用非线性滤波和改进的FCM算法优化分水岭变换得出的初始分割,提出了一种新的基于混合分割算法——IHWF(Improved Hybrid Watershed and FCM)分割法。与MeanShift算法及区域合并算法相比,该方法充分利用了区域的灰度和区域间的空间信息。试验结果表明该算法能有效克服分水岭算法的过分割问题,且分割效果优于以上两种方法。

Watershed transformation is a powerful morphological tool for image segmentation which can automatically generate a series of closed segmentation regions. However, the watershed transformation might give rise to over segmentation and it sensitive to noise. In order to overcome the inherent drawback of watershed algorithm-over-segmentation, a new method hybrid method of image segmentation IHWF （Improved Hybrid Watershed and FCM） is proposed, which uses non-linear filter algorithm and a modifies FCM algorithm to improve initial segmentation result obtained by the watershed transformation. This method uses information of regions gray and information between regions sufficiently compared with MeanShift and region-merge method. Experiment result shows the proposed algorithm overcome the watershed algorithm =over -segmentation efficiently and obtain good segmentation.