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乳腺密度的自动分割

Automatic segmentation of mammographic density.

作者信息

Sivaramakrishna R, Obuchowski N A, Chilcote W A, Powell K A

机构信息

Department of Biomedical Engineering, Lerner Research Institute, Cleveland, OH, USA.

出版信息

Acad Radiol. 2001 Mar;8(3):250-6. doi: 10.1016/S1076-6332(03)80534-2.

DOI:10.1016/S1076-6332(03)80534-2
PMID:11249089
Abstract

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate a completely automatic method, based on Kittler's optimal threshold, to estimate breast density by using the mammographers' definition.

MATERIALS AND METHODS

Thirty-two normal, right-craniocaudal-view mammograms of women aged 37-86 years were digitized. The whole breast area was segmented by using Kittler's optimal threshold procedure, and the dense portions were then segmented by using a modified version of Kittler's method. Segmentation results were validated by three independent mammographers who provided a signed percentage (in steps of 5%) to indicate the difference between their own visual estimation of the dense portions and the results obtained with the algorithm. The difference between the algorithm measurements and the mammographers' measurements was compared to the interobserver differences.

RESULTS

A high correlation was found between the algorithm measured density and the mammographers' measurements. Spearman correlations ranged from 0.92 to 0.95 (P < .001). Algorithm-measured density differed from the mammographers' measurements by an average of 6.9% (ie, average of the absolute differences). In contrast, mammographers' measurements differed between themselves by an average of 5.4%.

CONCLUSION

The difference between density as measured with the algorithm and as measured by the mammographers is similar to the differences observed between mammographers. This algorithm could be useful in providing clinically accurate estimates of breast density.

摘要

原理与目的

本研究旨在评估一种基于基特勒最优阈值的全自动方法,该方法采用乳腺造影师的定义来估计乳腺密度。

材料与方法

对32例年龄在37 - 86岁女性的右侧头尾位正常乳腺造影片进行数字化处理。采用基特勒最优阈值程序分割整个乳腺区域,然后使用基特勒方法的改进版本分割致密部分。由三名独立的乳腺造影师对分割结果进行验证,他们提供一个带符号的百分比(以5%为步长),以表明他们自己对致密部分的视觉估计与算法获得的结果之间的差异。将算法测量结果与乳腺造影师的测量结果之间的差异与观察者间差异进行比较。

结果

算法测量的密度与乳腺造影师的测量结果之间存在高度相关性。斯皮尔曼相关系数范围为0.92至0.95(P < .001)。算法测量的密度与乳腺造影师的测量结果平均相差6.9%(即绝对差异的平均值)。相比之下,乳腺造影师之间的测量结果平均相差5.4%。

结论

算法测量的密度与乳腺造影师测量的密度之间的差异与乳腺造影师之间观察到的差异相似。该算法可用于提供临床上准确的乳腺密度估计。

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