X-Ray Image Compression Using Convolutional Recurrent Neural Networks
This work addresses storage challenges in teleradiology by providing an effective compression method for medical images, though it appears incremental as it builds on existing deep learning approaches.
The authors tackled the problem of compressing high-resolution X-ray images for healthcare storage by proposing a Convolutional Recurrent Neural Network (RNN-Conv) method, which achieved improved compression performance with higher SSIM and PSNR metrics compared to state-of-the-art techniques on the NIH ChestX-ray8 dataset.
In the advent of a digital health revolution, vast amounts of clinical data are being generated, stored and processed on a daily basis. This has made the storage and retrieval of large volumes of health-care data, especially, high-resolution medical images, particularly challenging. Effective image compression for medical images thus plays a vital role in today's healthcare information system, particularly in teleradiology. In this work, an X-ray image compression method based on a Convolutional Recurrent Neural Networks RNN-Conv is presented. The proposed architecture can provide variable compression rates during deployment while it requires each network to be trained only once for a specific dimension of X-ray images. The model uses a multi-level pooling scheme that learns contextualized features for effective compression. We perform our image compression experiments on the National Institute of Health (NIH) ChestX-ray8 dataset and compare the performance of the proposed architecture with a state-of-the-art RNN based technique and JPEG 2000. The experimental results depict improved compression performance achieved by the proposed method in terms of Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics. To the best of our knowledge, this is the first reported evaluation on using a deep convolutional RNN for medical image compression.