基于全局特征和尺度不变特征转换特征融合的医学图像检索

Retrieval of medical images based on fusion of global feature and scale-invariant feature transform feature

特征提取是图像检索或图像配准的关键步骤,针对单一特征不能很好地表述图像的问题,根据医学图像的特点,提出了一种融合全局特征和局部特征的医学图像检索算法。首先在研究单一特征医学图像检索算法的基础上,提出了融合全局特征和相关反馈的检索算法;其次对尺度不变特征转换(SIFT)特征进行了优化,提出了改进的SIFT特征提取算法和匹配算法;最后,为了保证结果的准确性并改进检索效果,采用了融合局部特征的方法逐步求精。通过对标准临床数字式X射线成像(DR)图像数据库的实验研究表明,该算法应用在医学图像的检索中有较好的结果。

Feature extraction is a key step of image retrieval and image registration, but the single feature can not express the information of medical images efficiently. To overcome this shortcoming, a new algorithm for medical image retrieval combining global features with local features was proposed based on the characteristics of medical images. First, after studying the medical image retrieving techniques with single feature, a new retrieval method was proposed by considering global feature and relevance feedback. Then to optimize the Scale-Invariant Feature Transform（ SIFT） features, an improved SIFT features extraction and matching algorithm was proposed. Finally, in order to ensure the accuracy of the results and improve the retrieval result, local features were used for stepwise refinement. The experimental results on general Digital Radiography（ DR） images prove the effectiveness of the proposed algorithm.