Application of Artificial Neural Network Models in Segmentation and Classification of Nodules in Breast Ultrasound Digital Images
Application of Artificial Neural Network Models in Segmentation and Classification of Nodules in Breast Ultrasound Digital Images
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DOI：
10.1155/2016/7987212
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发表时间：
2016-01-01
影响因子：
7.6
通讯作者：
Schiabel, Homero
中科院分区：
文献类型：
--
作者：
Marcomini, Karem D.;Carneiro, Antonio A. O.;Schiabel, Homero
这项研究提出了一种自动检测和表征乳房超声检查结果的方法。我们对从组织模拟材料制成的乳房模型获得的超声图像进行了测试。当结果相当可观时，我们将相同的技术应用于临床检查。该过程开始时采用预处理（维纳滤波器、均衡和中值滤波器）来最大限度地减少噪声。然后，研究了五种分割技术，以确定病变轮廓的最简洁表示，使我们能够将神经网络 SOM 视为最相关的。划定对象后，最具表现力的特征被定义为所发现的形态描述，生成神经多层感知器 (MLP) 分类器的输入数据。使用模拟图像进行训练期间达到的准确率为 94.2%，AUC 为 0.92。为了评估数据泛化，使用一组未知图像对系统（模拟器和临床试验）进行分类，准确率分别为 90% 和 81%。所提出的分类器被证明是乳腺超声诊断的重要工具。
This research presents a methodology for the automatic detection and characterization of breast sonographic findings. We performed the tests in ultrasound images obtained from breast phantoms made of tissue mimicking material. When the results were considerable, we applied the same techniques to clinical examinations. The process was started employing preprocessing (Wiener filter, equalization, and median filter) to minimize noise. Then, five segmentation techniques were investigated to determine the most concise representation of the lesion contour, enabling us to consider the neural network SOM as the most relevant. After the delimitation of the object, the most expressive features were defined to the morphological description of the finding, generating the input data to the neural Multilayer Perceptron (MLP) classifier. The accuracy achieved during training with simulated images was 94.2%, producing an AUC of 0.92. To evaluating the data generalization, the classification was performed with a group of unknown images to the system, both to simulators and to clinical trials, resulting in an accuracy of 90% and 81%, respectively. The proposed classifier proved to be an important tool for the diagnosis in breast ultrasound.