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乳腺X线照片的联合图像专家组(JPEG)兼容数据压缩。

Joint photographic experts group (JPEG) compatible data compression of mammograms.

作者信息

Good W F, Maitz G S, Gur D

机构信息

Department of Radiology, University of Pittsburgh, PA 15261-0001.

出版信息

J Digit Imaging. 1994 Aug;7(3):123-32. doi: 10.1007/BF03168505.

Abstract

We have developed a Joint Photographic Experts Group (JPEG) compatible image compression scheme tailored to the compression of digitized mammographic images. This includes a preprocessing step that segments the tissue area from the background, replaces the background pixels with a constant value, and applies a noise-removal filter to the tissue area. The process was tested by performing a just-noticeable difference (JND) study to determine the relationship between compression ratio and a reader's ability to discriminate between compressed and noncompressed versions of digitized mammograms. We found that at compression ratios of 15:1 and below, image-processing experts are unable to detect a difference, whereas at ratios of 60:1 and above they can identify the compressed image nearly 100% of the time. The performance of less specialized viewers was significantly lower because these viewers seemed to have difficulty in differentiating between artifact and real information at the lower and middle compression ratios. This preliminary study suggests that digitized mammograms are very amenable to compression by techniques compatible with the JPEG standard. However, this study was not designed to address the efficacy of image compression process for mammography, but is a necessary first step in optimizing the compression in anticipation of more elaborate reader performance (ROC) studies.

摘要

我们开发了一种与联合图像专家组(JPEG)兼容的图像压缩方案,专门用于数字化乳腺X线图像的压缩。这包括一个预处理步骤,即从背景中分割出组织区域,用一个恒定值替换背景像素,并对组织区域应用去噪滤波器。通过进行一项恰可察觉差异(JND)研究来测试该过程,以确定压缩率与读者区分数字化乳腺X线照片压缩版本和未压缩版本能力之间的关系。我们发现,在15:1及以下的压缩率下,图像处理专家无法检测到差异,而在60:1及以上的压缩率下,他们几乎100%的时间都能识别出压缩图像。不太专业的观察者的表现明显较低,因为这些观察者在较低和中等压缩率下似乎难以区分伪像和真实信息。这项初步研究表明,数字化乳腺X线照片非常适合通过与JPEG标准兼容的技术进行压缩。然而,这项研究并非旨在解决乳腺摄影图像压缩过程的有效性问题,而是在预期进行更详尽的读者表现(ROC)研究时优化压缩的必要第一步。

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