Wavelet compression algorithm applied to abdominal ultrasound images

Abstract

We sought to investigate acceptable compression ratios of lossy wavelet compression on 640 × 480 × 8 abdominal ultrasound (US) images. We acquired 100 abdominal US images with normal and abnormal findings from the view station of a 932‐bed teaching hospital. The US images were then compressed at quality factors (QFs) of 3, 10, 30, and 50 followed outcomes of a pilot study. This was equal to the average compression ratios of 4.3:1, 8.5:1, 20:1 and 36.6:1, respectively. Four objective measurements were carried out to examine and compare the image degradation between original and compressed images. Receiver operating characteristic (ROC) analysis was also introduced for subjective assessment. Five experienced and qualified radiologists as reviewers blinded to corresponding pathological findings, analysed paired 400 randomly ordered images with two 17‐inch thin film transistor/liquid crystal display (TFT/LCD) monitors. At ROC analysis, the average area under curve (Az) for US abdominal image was 0.874 at the ratio of 36.6:1. The compressed image size was only 2.7% for US original at this ratio. The objective parameters showed the higher the mean squared error (MSE) or root mean squared error (RMSE) values, the poorer the image quality. The higher signal‐to‐noise ratio (SNR) or peak signal‐to‐noise ratio (PSNR) values indicated better image quality. The average RMSE, PSNR at 36.6:1 for US were 4.84 ± 0.14, 35.45 dB, respectively. This finding suggests that, on the basis of the patient sample, wavelet compression of abdominal US to a ratio of 36.6:1 did not adversely affect diagnostic performance or evaluation error for radiologists' interpretation so as to risk affecting diagnosis

Analysis of Wavelet-based Transform Compression Techniques on Medical Image

“…An effectively high compression scheme for reconstructing X-ray images without apparent human perceptual degradation or loss of fidelity is one of the key requirements to fully leverage low bit-rate and high throughput transmission and storage of medical images [4,5]. Image compression is an essential steps towards alleviating the problems of large demands for image data storage and transmission capability, which is a major challenge for applications related to Biomedical, Telemedicine, Teleconsultation, Teleradiology and Telecommunication.…”