基于U-Net的颅内出血识别算法

针对颅脑计算机断层成像（CT）影像中脑出血的分析和识别，提出采用神经网络模型U-Net与轮廓识别相结合的方法提取脑实质区域，通过阈值分割算法分析血块的图像纹理特征，并过滤软组织、脑组织和脑脊液等无关生理组织结构，实现对颅内出血点的精确定位，最后采用插值方法将出血区域进行三维重建，对血块的三维形态作出评估.在天津市某医疗机构提供的500例颅脑CT数据上进行了验证测试，实验结果表明，该算法达到97.4%的目标识别准确率，能够为脑出血诊断提供参考.     

A Neural Network Approach to Blind Estimation of PN Spreading Sequence in DS/SS Signals

As the power spectrum density of direct sequence spread spectrum (DS/SS or DS), signals is often much lower than the noises’, the DS signals have the ability to resist interception and interference. But in point of fact, if we have get some parameters of the DS signals, including information symbol period, chip period of the pseudo noise (PN) sequence, we can estimate the PN sequence in DS signals blindly, this has great value for management or interception of DS communications. Ref.[1…