自动量化评估小鼠脊背皮窗血管损伤对于血管靶向光动力疗法（V-PDT）的个性化精准治疗研究具有重要意义。V-PDT治疗过程中，受小鼠呼吸、心跳和不自主运动等因素影响，小鼠脊背皮窗窄带光血管图像会发生不同程度的非刚性形变，从而引起血管损伤量化误差。结合基于特征和灰度配准方法的优点，提出了一种判断式图像混合配准算法。首先基于配准组别图像均方差值（MSD）区分剧烈形变和轻微形变。对于轻微形变，直接采用微分同胚Demons（Log-Demons）算法进行配准；对于剧烈形变，先采用speeded up robust features thin-plate splines（SURF-TPS）算法进行全局配准，随后采用Log-Demons算法进行局部配准。实验结果表明，与SURF-TPS、Log-Demons及SURF-TPS+Log-Demons算法相比，所提算法既有最优的配准精度，又有较高的时间处理效率，而且能有效校正剧烈形变所导致的血管损伤评估误差。

Automatic quantitative assessment of vascular damage in mice's dorsal skin-fold window chamber (DSWC) plays an important role in personalized and precise vascular targeted photodynamic therapy (V-PDT). The narrow-band light blood vessel image of DSWC, however, will produce nonrigid deformations in varying degrees, which is caused by breathing, heartbeat, and involuntary movement of mice during V-PDT, and this leads to quantification errors of vascular damage. A judgment hybrid registration method combining the advantages of feature-based and gray-scale registration methods was proposed. The slight and severe deformations were first distinguished using the mean square difference (MSD) of registration groups. For slight deformation, the differential homeomorphism Demons (Log-Demons) algorithm was directly adopted for image registration. For severe deformation, the speeded up robust features thin-plate splines (SURF-TPS) algorithm was used for global registration, and then the Log-Demons algorithm was used to realize local registration. The experimental results show that the proposed algorithm has better registration accuracy and higher processing efficiency than the SURF-TPS, Log-Demons, and SURF-TPS+Log-Demons algorithm. Furthermore, this algorithm could effectively correct the inaccuracy assessment of vascular injury caused by severe deformation.