Kallergi Maria, Lucier Bradley J, Berman Claudia G, Hersh Marla R, Kim Jihai J, Szabunio Margaret S, Clark Robert A
Department of Radiology, College of Medicine, H. Lee Moffitt Cancer Center & Research Institute, University of South Florida, Tampa, FL 33612-4799, USA.
Radiology. 2006 Jan;238(1):62-73. doi: 10.1148/radiol.2381040896.
To evaluate the accuracy of a visually lossless, image-adaptive, wavelet-based compression method for achievement of high compression rates at mammography.
The study was approved by the institutional review board of the University of South Florida as a research study with existing medical records and was exempt from individual patient consent requirements. Patient identifiers were obliterated from all images. The study was HIPAA compliant. An algorithm based on scale-specific quantization of biorthogonal wavelet coefficients was developed for the compression of digitized mammograms with high spatial and dynamic resolution. The method was applied to 500 normal and abnormal mammograms from 278 patients who were 32-85 years old, 85 of whom had biopsy-proved cancer. Film images were digitized with a charge-coupled device-based digitizer. The original and compressed reconstructed images were evaluated in a localization response operating characteristic experiment involving three radiologists with 2-10 years of experience in reading mammograms.
Compression rates in the range of 14:1 to 2051:1 were achieved, and the rates were dependent on the degree of parenchymal density and the type of breast structure. Ranges of the area under the receiver operating characteristic curve were 0.70-0.83 and 0.72-0.86 for original and compressed reconstructed mammograms, respectively. Ranges of the area under the localization response operating characteristic curve were 0.39-0.65 and 0.43-0.71 for original and compressed reconstructed mammograms, respectively. The localization accuracy increased an average of 6% (0.04 of 0.67) with the compressed mammograms. Localization performance differences were statistically significant with P = .05 and favored interpretation with the wavelet-compressed reconstructed images.
The tested wavelet-based compression method proved to be an accurate approach for digitized mammography and yielded visually lossless high-rate compression and improved tumor localization.
评估一种视觉无损、图像自适应、基于小波的压缩方法在乳腺钼靶摄影中实现高压缩率的准确性。
该研究经南佛罗里达大学机构审查委员会批准，作为一项对现有病历的研究，无需患者个人同意。所有图像均去除了患者标识符。该研究符合《健康保险流通与责任法案》（HIPAA）要求。开发了一种基于双正交小波系数特定尺度量化的算法，用于压缩具有高空间和动态分辨率的数字化乳腺钼靶图像。该方法应用于278例年龄在32至85岁的患者的500张正常和异常乳腺钼靶图像，其中85例经活检证实患有癌症。胶片图像通过基于电荷耦合器件的数字化仪进行数字化。在一项定位反应操作特征实验中，由三位具有2至10年乳腺钼靶阅片经验的放射科医生对原始图像和压缩重建图像进行评估。
实现了14:1至2051:1的压缩率，且压缩率取决于实质密度程度和乳腺结构类型。原始乳腺钼靶图像和压缩重建乳腺钼靶图像的受试者操作特征曲线下面积范围分别为0.70 - 0.83和0.72 - 0.86。原始乳腺钼靶图像和压缩重建乳腺钼靶图像的定位反应操作特征曲线下面积范围分别为0.39 - 0.65和0.43 - 0.71。使用压缩乳腺钼靶图像时，定位准确性平均提高了6%（从0.67的0.04提高到0.04）。定位性能差异具有统计学意义（P = 0.05），且小波压缩重建图像的解读更具优势。
经测试的基于小波的压缩方法被证明是数字化乳腺钼靶摄影的一种准确方法，可实现视觉无损的高速率压缩并改善肿瘤定位。
