提出一种采用多方向梯度及其二阶梯度描述神经切片图像的纹理特征,进而识别神经功能束类型的方法。首先,在神经切片图像随机选择一些像素,获得这些像素在邻域范围内4个方向上的梯度和二阶梯度的变化曲线;其次,提取这些曲线的周期和幅值作为描述这些随机选择像素邻域的特征;再次,采用粗糙K均值算法对这些随机选择像素进行聚类处理,从而把功能束区分为不同的类型;最后,分析了在此过程中两个参数对功能束类型区分结果的影响。实验结果表明,所提出的纹理特征描述方法可以准确区分神经切片图像中不同类型的功能束,所提出的识别算法不仅能有效识别神经功能束的类型,而且识别结果与所需设置的参数无关,因此,具有比较强的适应性。

An approach to recognize types of fascicular groups from nerve slice image by the gray level multi-direction gradient and its 2nd derivative gradient is proposed in this paper. First, some pixels are selected arbitrarily from a slice image and the gray level multi-direction gradient and the 2nd derivative gradient of their neighborhood areas are calculated. Then the frequency and amplitude of the multi-direction gradient and 2nd derivative gradient curves are extracted as the texture features of the arbitrarily selected pixels neighborhood. Second, the algorithm for recognizing the types of fascicular groups in nerve slice image is proposed based on the improved rough K-means clustering.The parameters influencing the recognition results are analyzed. The experimental results show that the approach can not only classify the types of fascicular groups accurately but the recognizing results are unrelated with the parameters, which reflect its good adaptivity.