Computerized lesion detection on breast ultrasound
Computerized lesion detection on breast ultrasound
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DOI：
10.1118/1.1485995
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发表时间：
2002-07-01
期刊：
影响因子：
3.8
通讯作者：
Mendelson, EB
中科院分区：
文献类型：
--
作者：
Drukker, K;Giger, ML;Mendelson, EB
我们研究了使用径向梯度指数（RGI）过滤技术来自动检测乳腺超声病变。在初始RGI滤波后，在400例患者（757张图像）的数据库中，在每张图像0.76次假阳性检测时，灵敏度为87%。下一个。通过最大化从检测点生长的区域的平均径向梯度（ARD）指数，从背景对候选病变进行缝合，与放射科医生的病变轮廓重叠0.4,75%的病变被正确检测到。随后，使用循环分析来评估贝叶斯神经网络将候选病变分类为实际病变和假阳性的质量。循环分析产生的A值为0.84，在每张图像0.48个假阳性时，总体性能为94%的灵敏度。使用计算机化的乳房超声分析可能最终促进超声检查在乳腺癌筛查项目中的应用。(C) 2002年美国医学物理学家协会。
We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next. lesion candidates were seamented from the back-round by maximizing an average radial gradient (ARD) index for regions grown from the detected points, At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an A, value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs. (C) 2002 American Association of Physicists in Medicine.