基于曲线波的超声图像分割

为了提高前列腺超声图像分割的准确率,提出一种基于曲线波的半监督超声图像自动分割方法.首先,采用对微小波动敏感度高的Riemann-Liouville (RL)分数阶微分算子,突出模糊边界并增强超声图像的纹理；其次,运用曲线波变换对超声图像进行频域中的分解,获得不同子带分量以表达超声图像特征；然后,基于Adaboost的分类算法识别出超声图像中的病灶区和非病灶区；最后,采用中值滤波和腐蚀的方法使病灶区域边缘完整、平滑.实验表明,与运用共生矩阵及二进小波作纹理分析的分割结果比较,所提出的方法在准确率上有了很大的改进,分割超声图像效果更佳.

Ultrasound image segmentation based on curvelet

In order to improve the accuracy of prostate ultrasound image segmentation,a semi-supervised automatic segmentation method based on curvelet transform is proposed.First,the Riemann-Liouville(RL) fractional differential operator which is sensitive to the tiny fluctuations is used to enhance the fuzzy boundary and image texture.Secondly,the image is transformed into curvelet domain and different subbands are obtained to represent the ultrasound image characteristics.Thirdly,the Adaboost algorithm is applied to identify the lesion and non-lesion regions in the ultrasound image.Finally,the median filter and the erosion operator are used to smooth the lesion regions’ edge.Experiments show that the proposed method outperforms the approaches based on co-occurrence matrix and dyadic wavelet in terms of accuracy.