全乳超声冠状扫描中乳头定位的集成学习方法。

IF 2.5 4区 医学 Q1 ACOUSTICS
Alex Noel Joseph Raj, Ruban Nersisson, Vijayalakshmi G V Mahesh, Zhemin Zhuang
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引用次数: 0

摘要

乳头是乳腺病变诊断的重要标志。虽然有先进的计算机辅助检测(CADe)系统可以在乳房x线摄影图像的中外侧斜位(MLO)视图中检测乳头,但很少有学术著作涉及乳房超声(BUS)图像的冠状视图。本文介绍了一种新型的超声成像乳头影区定位系统。通过迭代滑动窗口计算Hu矩和灰度共生矩阵(GLCM),提取形状和纹理特征。然后将这些特征连接并输入到人工神经网络(ANN)中以获得可能的NSA。然后,计算轮廓特征,如分形维数的形状复杂度、边缘到外围的距离和轮廓面积,并将其传递给支持向量机(SVM),以识别每种情况下准确的NSA。冠状面BUS数据集是建立在我们自己的基础上的,该数据集由来自13名患者的64张图像组成。测试结果表明,该系统的准确率为91.99%,特异性为97.55%,灵敏度为82.46%,F-score为88%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning.

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA's. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.

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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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