Automated Detection of Breast Mass Spiculation Levels and Evaluation of Scheme Performance
Automated Detection of Breast Mass Spiculation Levels and Evaluation of Scheme Performance
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DOI：
10.1016/j.acra.2008.07.015
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发表时间：
2008-12-01
影响因子：
4.8
通讯作者：
Zheng, Bin
中科院分区：
文献类型：
--
作者：
Jiang, Luan;Song, Enmin;Zheng, Bin
基本原理和目标。尽管乳房肿块边界的毛刺水平是乳房X线照相术检测到的肿块恶性肿瘤的主要标志，但开发一种自动计算机化方法来检测毛刺水平并定量评估这种方法的性能是一项艰巨的任务。本研究的目的是 (1) 开发和测试一种新方法来改进质量分割和检测质量边界毛刺水平，以及 (2) 使用相对较大的成像数据集评估该方法的性能。材料和方法。为此研究开发的全自动方法包括三个图像处理步骤。第一步，在校正背景趋势后，在选定的感兴趣区域（ROI）中应用最大熵原理，以增强质量的初始轮廓。在第二步中，使用活动轮廓模型来细化初始轮廓。在第三步中，使用特殊的线检测器检测并识别连接到质量边界的针状线。计算定量毛刺指数以评估毛刺程度。为了开发和评估这种自动化方法，从公开的图像数据库中提取了 211 个描述质量的 ROI。在这些 ROI 中，106 个描绘了外接质量区域，105 个涉及针状质量区域。使用接受者操作特征 (ROC) 分析评估该方法的性能。结果。当将该方法应用于数据集时，ROC 曲线下的计算面积为 0.701 +/- 0.027。通过将毛刺指数设置为5.0的阈值，该方法的总体分类准确率达到66.4%。敏感性为 54.3%，特异性为 78.3%。结论。在这项研究中，开发了一种具有许多独特特征的新计算机化方法来检测毛刺质量区域，并应用简单的毛刺指数来量化毛刺水平。尽管该定量指数可用于区分针状质量和外接质量，但结果还表明质量针状水平的自动检测仍然是一个技术挑战。
Rationale and Objectives. Although the spiculation levels of breast mass boundaries are a primary sign of malignancy for masses detected on mammography, developing an automated computerized method to detect spiculation levels and quantitatively evaluation the performance of such a method is a difficult task. The objectives of this study were to (1) develop and test a new method to improve mass segmentation and detect mass boundary spiculation levels and (2) assess the performance of this method using a relatively large imaging data set.Materials and Methods. The fully automated method developed for this study includes three image-processing steps. In the first step, the principle of maximum entropy is applied in the selected region of interest (ROI) after correcting the background trend to enhance the initial outlines of a mass. In the second step, an active-contour model is used to refine the initial outlines. In the third step, spiculated lines connected to the mass boundary are detected and identified using a special line detector. A quantitative spiculation index is computed to assess the degree of spiculation. To develop and evaluate this automated method, 211 ROIs depicting masses were extracted from a publicly available image database. Among these ROIs, 106 depicted circumscribed mass regions and 105 involved spiculated mass regions. The performance of the method was evaluated using receiver-operating characteristic (ROC) analysis.Results. The computed area under the ROC curve, when applying the method to the data set, was 0.701 +/- 0.027. By setting up a threshold at a spiculation index of 5.0, the method achieved an overall classification accuracy of 66.4%. with 54.3% sensitivity and 78.3% specificity.Conclusions. In this study, a new computerized method with a number of unique characteristics was developed to detect spiculated mass regions, and a simple spiculation index was applied to quantify mass spiculation levels. Although this quantitative index can be used to distinguish between spiculated and circumscribed masses, the results also suggest that the automated detection of mass spiculation levels remains a technical challenge.