基于BP神经网络的全身骨SPECT图像分割

Segmentation of Whole Body Bone SPECT Image Based on BP Neural Network

我们尝试用BP神经网络分割全身骨SPECT图像,实现病变区域的自动识别。由于SPECT图像病变区域的判断要考虑图像的整体信息,其特征具有不确定性,单靠神经网络识别很难达到理想的效果。我们把分割过程分预处理、神经网络分割和后处理三部分,先用最佳阈值法进行预处理,然后用神经网络进行粗分类,最后用模板匹配和去对称程序排除误识别的区域。

In this paper, BP neural network is used to segment whole body bone SPECT image so that the lesion area can be recognized automatically. For the uncertain characteristics of SPECT images, it is hard to achieve good segmentation result if only the BP neural network is employed. Therefore, the segmentation process is divided into three steps： first, the optimal gray threshold segmentation method is employed for preprocessing, then BP neural network is used to roughly identify the lesions, and finally template match method and symmetry- removing program are adopted to delete the wrongly recognized areas.