Abstract
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.
Abstract (translated)
随着数字健康革命的到来,每天都在生成、存储和处理大量的临床数据。这使得存储和检索大量的医疗保健数据,特别是高分辨率医学图像变得尤为困难。因此,对医学图像进行有效的图像压缩在当今的医疗信息系统,特别是在远程放射学中起着至关重要的作用。本文提出了一种基于卷积递归神经网络RNN-Conv的X射线图像压缩方法。所提出的架构可以在部署期间提供可变的压缩率,但它要求每个网络仅针对特定尺寸的X射线图像进行一次训练。该模型使用一个多级池方案,学习上下文化的特性以实现有效的压缩。我们在美国国立卫生研究院(NIH)ChestX-Ray8数据集上进行了图像压缩实验,并将所提出的体系结构的性能与最先进的基于RNN的技术和jpeg 2000进行了比较。实验结果表明,该方法在结构相似性指数(ssim)和峰值信噪比(psnr)指标方面提高了压缩性能。据我们所知,这是首次报道的使用深度卷积RNN进行医学图像压缩的评估。
URL
https://arxiv.org/abs/1904.12271