Lossless medical ultrasound image compression based on frequency domain decomposition

Medical ultrasound imaging is a widely used non-invasive method for diagnosing diseases. However, these images contain significant speckle noise, which differs from the characteristics of natural images. This makes effective lossless compression of medical ultrasound images a challenging task. In this paper, we propose a novel hybrid ultrasound image lossless learning compression framework. Firstly, we use the traditional DCT (discrete cosine transform) to transform the original raw pixels of ultrasound images into the frequency domain. Secondly, to effectively compress the numerical values in the frequency domain, we decompose the DCT coefficients into different groups to reduce local and global information redundancy in the frequency domain. Finally, we use learned and non-learned methods to compress the DCT coefficients of different groups separately. The experimental results show that on the Breast ultrasound image dataset, our proposed method achieves a bit rate reduction of 8.6% to 68.9% compared to learned and non-learned methods. •We propose a novel hybrid medical ultrasound image lossless learning compression framework.•We design a frequency domain DCT (discrete cosine transform) coefficients decomposition method.•We design a simple and effective probability estimation network for entropy coding.