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首页> 外文期刊>Radiology >Free-response receiver operating characteristic evaluation of Lossy JPEG2000 and object-based set partitioning in hierarchical trees compression of digitized mammograms.
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Free-response receiver operating characteristic evaluation of Lossy JPEG2000 and object-based set partitioning in hierarchical trees compression of digitized mammograms.

机译：有损JPEG2000的自由响应接收器工作特性评估以及数字化X线照片的分层树压缩中基于对象的集合划分。

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PURPOSE: To assess the effects of two irreversible wavelet-based compression algorithms--Joint Photographic Experts Group (JPEG) 2000 and object-based set partitioning in hierarchical trees (SPIHT)--on the detection of clusters of microcalcifications and masses on digitized mammograms. MATERIALS AND METHODS: The use of the images in this retrospective image-collection study was approved by the institutional review board, and patient informed consent was not required. One hundred twelve mammographic images (28 with one or two clusters of microcalcifications, 19 with one mass, 17 with both abnormal findings, and 48 with normal findings) obtained in 60 women who ranged in age from 25 to 79 years were digitized and compressed at 40:1 and 80:1 by using the JPEG2000 and object-based SPIHT methods. Five experienced radiologists were asked to locate and rate clusters of microcalcifications and masses on the original and compressed images in a free-response receiver operating characteristic (FROC) data acquisition paradigm. Observer performance was evaluated with the jackknife FROC method. RESULTS: The mean FROC figures of merit for detecting clusters of microcalcifications, masses, and both radiographic findings on uncompressed images were 0.80, 0.81, and 0.72, respectively. With object-based SPIHT 80:1 compression, the corresponding values were larger than the values for uncompressed images by 0.005, 0.009, and -0.005, respectively. The 95% confidence interval for the differences in figures of merit between compressed and uncompressed images was -0.039, 0.033 for the microcalcification finding; -0.055, 0.034 for the mass finding; and -0.039, 0.030 for both findings. Because each of these confidence intervals includes zero, no significant difference in detection accuracy between uncompressed and object-based SPIHT 80:1 compression was observed at a P value of 5%. The F test of the null hypothesis that all of the modes (uncompressed and four compressed modes) were equivalent yielded the following results: F= 0.255, P = .903 for the microcalcification finding; F = 0.340, P = .848 for the mass finding; and F = 0.122, P = .975 for both findings. CONCLUSION: To within the accuracy of these measurements, lossy compression of digital mammographic data at 80:1 with JPEG2000 or the object-based SPIHT algorithm can be performed without decreasing the rate of detection of clusters of microcalcifications and masses.
机译：目的：评估两种不可逆的基于小波的压缩算法-联合图像专家组（JPEG）2000和分层树中的基于对象的集合划分（SPIHT）-对数字化乳房X线照片上的微钙化和质量簇的检测。材料与方法：该回顾性图像收集研究中的图像使用已获得机构审查委员会的批准，并且无需患者知情同意。在60位年龄在25至79岁之间的女性中获得了112幅乳腺X线照片（28幅带有一或两个微钙化簇，19幅肿块，17幅既有异常发现又有48幅具有正常发现），并在使用JPEG2000和基于对象的SPIHT方法实现40：1和80：1。要求五名经验丰富的放射科医生在自由响应的接收器操作特征（FROC）数据采集范例中，对原始和压缩图像上的微钙化和肿块进行定位和评级。用折刀FROC方法评估观察者的性能。结果：用于检测未压缩图像上的微钙化簇，肿块和两个射线照相结果的平均FROC品质因数分别为0.80、0.81和0.72。使用基于对象的SPIHT 80：1压缩时，对应的值比未压缩图像的值分别大0.005、0.009和-0.005。压缩图像和未压缩图像之间品质因数差异的95％置信区间为-0.039，微钙化发现值为0.033; -0.055，质量发现值为0.034;和-0.039，两个发现均为0.030。因为这些置信区间中的每一个都包括零，所以在P值为5％时，未压缩和基于对象的SPIHT 80：1压缩之间的检测精度没有显着差异。所有模式（未压缩模式和四个压缩模式）均等的零假设的F检验得出以下结果：F = 0.255，P = .903（用于微钙化发现）;对于质量发现，F = 0.340，P = .848;和F = 0.122，两个发现的P = .975。结论：在这些测量的精度范围内，可以使用JPEG2000或基于对象的SPIHT算法以80：1的速率对数字乳房X线照片数据进行有损压缩，而不会降低对微钙化和质量簇的检测率。

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