Yang Xiaofeng, Fei Baowei
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Proc SPIE Int Soc Opt Eng. 2012 Feb 23;8316:83162O. doi: 10.1117/12.912188.
We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 ± 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.
我们开发了一种基于纵向图像配准和机器学习的经直肠超声（TRUS）图像三维（3D）分割方法。利用每个患者的纵向图像，我们将先前获取的图像与同一受试者的新图像进行配准。使用三个正交的伽柏滤波器组从每个配准图像中提取纹理特征。来自配准图像的患者特异性伽柏特征用于训练核支持向量机（KSVM），然后对新获取的前列腺图像进行分割。该分割方法在五名患者的TRUS数据中进行了测试。我们的分割结果与手动分割之间的平均表面距离为1.18±0.31毫米，这表明我们基于纵向图像配准的自动分割方法对于在TRUS图像中分割前列腺是可行的。
