Content-based Image Retrieval for Breast Ultrasound Images using Convolutional Autoencoders: A Feasibility Study

Abstract

Ultrasound imaging is one of the most widely used medical imaging modalities for detecting breast cancer. However, the accuracy of diagnosing the tumors in breast ultrasound (BUS) images might vary based on the experience level of the radiologist. Content-based image retrieval (CBIR) systems can be employed to improve the diagnosis accuracy by providing the radiologist with BUS images of previous, clinically relevant cases. In this study, a CBIR system is developed based on deep learning technology to support the diagnosis of BUS images. In particular, each query BUS image submitted to the system is analyzed using a custom-made convolutional autoencoder (CAE) to extract a latent features vector that represents the image. An important advantage of the proposed CAE is its ability to extract the latent features vector without the need to localize or outline the tumor. The latent features vector of the query image is analyzed using a similarity measure to identify and retrieve the most relevant BUS images from a reference BUS image database. Finally, the retrieved BUS images are displayed to the radiologist. The performance of the proposed CBIR system has been evaluated using a set of BUS images that includes benign and malignant breast tumors. The results reported in this study suggest the feasibility of employing the proposed CBIR system to improve the diagnosis of BUS images.