doiSerbia

Convolutional neural networks and hash learning for feature extraction and of fast retrieval of pulmonary nodules

Qin Pinle (School of Data Science and Technology, North University of China, Taiyuan, China)
Chen Jun (School of Data Science and Technology, North University of China, Taiyuan, China)
Zhang Kai (School of Data Science and Technology, North University of China, Taiyuan, China)
Chai Rui (School of Data Science and Technology, North University of China, Taiyuan, China)

With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the “semantic gap” that exists between the low level visual information captured by imaging devices and high level semantic information perceived by the human. Using deep convolution neural network (CNN) to construct the CBMIR system can fully characterize the high level semantic features information for medical image retrieval. The existing network mostly used for the natural images can’t produce a good result directly applied to medical image. This paper used UNet method to preprocessing under the guidance of medical knowledge. Then, multi-scale receiving field convolution module is used to extract features of the segmented images with different sizes. Finally, encoded the features and used a coarse to fine search strategy with an average search accuracy of 0.73.

Keywords: Content Based Medical Image Retrieval (CBMIR), Convolutional Neural Networks (CNN), Similarity Measure, Deep Learning