Our study, involving a collaboration with radiologists (DK,NSK) as well as a leading international developer of medical imaging software (AGFA), is primarily concerned with improved methods of assessing the diagnostic quality of compressed medical images and the investigation of compression artifacts resulting from JPEG and JPEG2000. In this work, we compare the performances of the Structural Similarity quality measure (SSIM), MSE/PSNR, compression ratio CR and JPEG quality factor Q, based on experimental data collected in two experiments involving radiologists. An ROC and Kolmogorov-Smirnov analysis indicates that compression ratio is not always a good indicator of visual quality. Moreover, SSIM demonstrates the best performance, i.e., it provides the closest match to the radiologists' assessments. We also show that a weighted Youden index1 and curve tting method can provide SSIM and MSE thresholds for acceptable compression ratios.
The increasing use of technologies such as CT and MRI, along with a continuing improvement in their resolution,
has contributed to the explosive growth of digital image data being generated. Medical communities around the
world have recognized the need for efficient storage, transmission and display of medical images. For example,
the Canadian Association of Radiologists (CAR) has recommended compression ratios for various modalities and
anatomical regions to be employed by lossy JPEG and JPEG2000 compression in order to preserve diagnostic
quality.
Here we investigate the effects of the sharp skull edges present in CT neuro images on JPEG and JPEG2000
lossy compression. We conjecture that this atypical effect is caused by the sharp edges between the skull bone and
the background regions as well as between the skull bone and the interior regions. These strong edges create large
wavelet coefficients that consume an unnecessarily large number of bits in JPEG2000 compression because of its
bitplane coding scheme, and thus result in reduced quality at the interior region, which contains most diagnostic
information in the image. To validate the conjecture, we investigate a segmentation based compression algorithm
based on simple thresholding and morphological operators. As expected, quality is improved in terms of PSNR as
well as the structural similarity (SSIM) image quality measure, and its multiscale (MS-SSIM) and informationweighted
(IW-SSIM) versions. This study not only supports our conjecture, but also provides a solution to
improve the performance of JPEG and JPEG2000 compression for specific types of CT images.
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