Lung Nodule Image Retrieval Based on Convolutional Neural Networks and Hashing

Lung medical image retrieval based on content similarity plays an important role in computer-aided diagnosis of lung cancer. In recent years, binary hashing has become a hot topic in this field due to its compressed storage and fast query speed. Traditional hashing methods often rely on high-dimensional features based hand-crafted methods, which might not be optimally compatible with lung nodule images. Also, different hashing bits contribute to the image retrieval differently, and therefore treating the hashing bits equally affects the retrieval accuracy. Hence, an image retrieval method of lung nodule images is proposed with the basis on convolutional neural networks and hashing. First, a pre-trained and fine-tuned convolutional neural network is employed to learn multi-level semantic features of the lung nodules. Principal components analysis is utilized to remove redundant information and preserve informative semantic features of the lung nodules. Second, the proposed method relies on nine sign labels of lung nodules for the training set, and the semantic feature is combined to construct hashing functions. Finally, returned lung nodule images can be easily ranked with the query-adaptive search method based on weighted Hamming distance. Extensive experiments and evaluations on the dataset demonstrate that the proposed method can significantly improve the expression ability of lung nodule images, which further validates the effectiveness of the proposed method.