Automatic segmentation of breast lesions on ultrasound
Automatic segmentation of breast lesions on ultrasound
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DOI：
10.1118/1.1386426
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发表时间：
2001-08-01
期刊：
影响因子：
3.8
通讯作者：
Vyborny, CJ
中科院分区：
文献类型：
--
作者：
Horsch, K;Giger, ML;Vyborny, CJ
在本文中，我们提出了一种计算高效的超声乳腺肿块分割算法，该算法基于最大化通过预处理图像的灰度值阈值定义的分区边缘的效用函数。分割算法的性能通过两种方式在 400 个案例的数据库上进行评估。 400例中，复杂囊肿124例，良性实性病变182例，恶性病变94例。在第一次评估中，将计算机描绘的边缘与手动描绘的边缘进行比较。在重叠阈值为 0.40 时，分割算法正确描绘了 94% 的病灶。在第二次评估中，我们的计算机辅助诊断方法在计算机描绘的边缘上的性能与我们的方法在手动描绘的边缘上的性能进行了比较。在区分恶性和非恶性病变的任务中，循环评估在手动描绘的边缘和计算机描绘的边缘上分别产生A值0.90和0.87。 (C) 2001 年美国医学物理学家协会。
In this paper we present a computationally efficient segmentation algorithm for breast masses on sonography that is based on maximizing a utility function over partition margins defined through gray-value thresholding of a preprocessed image. The performance of the segmentation algorithm is evaluated on a database of 400 cases in two ways. Of the 400 cases, 124 were complex cysts, 182 were benign solid lesions, and 94 were malignant lesions. In the first evaluation, the computer-delineated margins were compared to manually delineated margins. At an overlap threshold of 0.40, the segmentation algorithm correctly delineated 94% of the lesions. In the second evaluation, the performance of our computer-aided diagnosis method on the computer-delineated margins was compared to the performance of our method on the manually delineated margins. Round robin evaluation yielded A, values of 0.90 and 0.87 on the manually delineated margins and the computer-delineated margins, respectively, in the task of distinguishing between malignant and nonmalignant lesions. (C) 2001 American Association of Physicists in Medicine.