基于相似性度量的肺结节图像检索算法

Image Retrieval Algorithm of Pulmonary Nodules Based on Similarity Measurement

为了克服肺部病变CT表现复杂,极易造成医生误诊的缺点,提出了一种基于相似性度量的医学图像检索算法并用于肺癌的诊断研究,该相似性度量保持了图像的语义相关和视觉相似.首先,根据相似性度量理论构建距离度量学习算法学习一个马氏距离;然后,根据学习的马氏距离度量,提出新的医学图像检索算法,并将提出的算法应用于肺癌的诊断研究.实验结果证明了该检索算法在肺癌诊断应用中的可行性和有效性.

In order to overcome the shortcomings that CT of pulmonary lesions is complex and is very easy to lead to misdiagnosis,a medical image retrieval algorithm based on similarity measurement was proposed to diagnose lung cancer.The similarity measurement maintains the semantic relevance and visual similarity of the image.Firstly,a distance metric learning algorithm was constructed to learn a Mahalanobis distance on the basis of the proposed similarity measurement.Secondly,a novel medical image retrieval algorithm was proposed based on the learned distance metric to diagnose lung cancer.The study results demonstrate the feasibility and effectiveness of the proposed retrieval algorithm in lung cancer diagnosis.