Convolutional auto-encoder and discrete wavelet transform for lossy region-based medical image compression

Abstract

Medical imaging is a vital and ever-evolving discipline that plays a critical role in detecting, diagnosing, and planning surgeries for diseases. In medical imaging, image compression systems are employed to reduce storage and bandwidth requirements. This paper presents a region of interest (ROI) based image compression method for brain MRI images by employing a convolutional auto-encoder (CAE) and discrete wavelet transform (DWT) to achieve both efficient compression and high-quality reconstruction in terms of lossy compression. Our compression approach involves separating image regions, employing a lossy compression technique based on CAE and DWT, with a lossless compression technique based arithmetic encoding. To achieve optimal compression of non-ROI regions, the encoding network of the CAE generates a compacted feature map that preserves structural information, which aids the DWT based codec and the decoding network of the CAE in reconstructing the output. To maintain diagnostically significant information, the ROI portion of the image is losslessly compressed using arithmetic encoding technique. To assess the effectiveness of our proposed method, we compared our results to standard lossy compression algorithms and recent approaches. Our method achieved a peak signal-to-noise ratio (PSNR) of 37.91 dB and a mean structural similarity index measure (MS-SSIM) of 98.62% at a high compression ratio of 30.