基于GFO和标记点分水岭算法的医学图像分割

传统的分水岭分割一般是在原始图像中根据边缘检测算子所得的边缘图进行计算,常规的边缘检测算子并没有引入图像的先验信息或形状约束。由于病理改变以及医学影像数据内在的模糊性,常规边缘检测算子很难引导分水岭算法收敛到正确的目标轮廓,由此导致传统分水岭算法容易受到图像噪声的干扰而"过分割"。在此情况下,提出利用基于广义模糊算子(GFO)的边缘检测算法来改进标记点分水岭分割。从实验结果看,提出的方法非常适合应用于临床医学图像分割。

Segmentation of medical images based on GFO and marker-controlled watershed

Tranditional watershed segmentation is implemented based on the edge-extracted image from the original image.The conventional edgedetecting algorithms have not prior information as well as shape restriction.Because the changing pathology and the inherent property of fuzzy imaging data,the conventional edge-detecting can hardly guide watershed algorithm converging into the correct object contour;the resultant watershed algorithm is subjected to image noise and over-segmentation.To improve marker-controlled watershed segmentation is proposed using the edge-detecting algorithm based on Generalized Fuzzy Operator.Seeing from the results,the proposed method is very adaptive to the segmentation of the clinical images.