目的 颈动脉粥样硬化斑块成分识别是预测脑血管疾病及脑卒中临床病发的主要依据,然而在实际中斑块成分识别会受到磁共振图像固有的噪声和多序列图像空间坐标不匹配的影响,为了更加精确地识别斑块的组成成分,提出基于小波分析和中值滤波相结合的去噪方法,以及基于图形上下文(shape context)的颈动脉多序列磁共振(MR)图像配准算法。方法 针对MR图像去噪,首先在传统小波去噪的基础上改进了阈值函数,根据高、低频子带噪声分布比重自适应的选取阈值。多序列MR图像配准方面,在提取多序列图像颈动脉血管边缘的基础上,用shape context描述子做血管形状匹配,依据产生的匹配点对进行迭代校正,计算参考图像与浮动图像的血管形变场,然后采用样条插值方法得到最终的配准结果。结果 利用本文去噪方法能有效地去除图像高、低频域的噪声,同时保护图像的原始细节。本文多序列MR图像血管配准方法使得配准后血管重合度达到了96%±0.8%。本文方法能够有效地提高MR图像的质量,验证了算法的有效性。结论 本文方法能够有效去除多序列磁共振图像噪声和空间位置不匹配的情况,本文去噪方法也适用于其他模态(CT,超声等)医学图像的斑点噪声以及噪声分布不均匀等情况下的噪声去除,本文配准方法也能够有效地处理基于小目标,对精度要求较高的精细配准问题。

Objective Carotid atherosclerosis plaque is the primary cause of heart disease and stroke. Thus, accurate identification of plaque component is the main basis for plaque rupture prediction. However, multiple sequence MR images are hampered by noise and misalignment. To solve the first problem, an improved denoising method based on wavelet transformation is used. Then, a registration method based on shape context method is proposed to accurately align multiple MR image sequence. Method We replace the traditional threshold function used in wavelet transform denoising with an adaptive threshold value that changes according to noise distribution in high-and low-frequency subband to achieve effective MRI denoising capabilities. Multiple MRI registration is performed by first identifying edge points with matching shape context. Then, an iterative procedure is invoked to obtain deformation field, which determines B-spline interpolated output image. Result The proposed denoising method is proved to remove image noise effectively while preserving original information. The overlapping accuracy among multiple sequences of the same artery reaches 96%±0.8%. Conclusion Results from the experiment show the effectiveness of the proposed method at enhancing image quality.