消除局部极值的多尺度形态学肝脏肿瘤CT图像分割方法

Liver tumor CT image segmentation method using multi-scale morphology of eliminating local minima

针对肝脏CT图像中因灰度不均、边缘模糊等造成肿瘤难以准确分割的问题,提出了一种消除局部极值的多尺度形态学分割方法。首先利用面积算子对图像进行预处理,在平滑图像的同时,保持目标区域的边缘信息;其次融合梯度图像中不同极值的邻域统计信息和形态属性(深度和尺度)区分极值,通过设定不同大小的结构元素,对不同极值采用多尺度结构元素进行闭运算,在消除伪局部极值的同时实现图像的自适应修正;最后利用分水岭变换分割肝脏肿瘤。实验结果表明,该方法能够在保持图像边缘的同时,有效减轻过分割现象,实现肝脏肿瘤的准确分割。

Many methods for liver tumor Computed Tomography （CT） segmentation have the difficulty to achieve accurate tumor due to inhomogeneous gray and fuzzy edges. To obtain precise segmentation result, a method using multi-scale morphology was proposed to eliminate local minima. Firstly, the morphological area operation was used to remove image＇s small burrs and irregular edges so as to avoid boundaries migration. Secondly, local minima in gradient image were distinguished by the combined knowledge of statistic characteristics and morphological properties including depth and scale. After partition, the function relationship was established between muhi-scale structure elements and local minima. In order to filter noise via large-size strueture elements and preserving major object via small-size structure elements, a morphological method called close operation was then employed to adaptively modify the image. Finally, standard watershed transform was utilized to implement segmentation of liver tumor. The experimental results show that this method can reduce over-segmentation effectively and liver tumor can be segmented accurately while boundaries of objects are located precisely.