Breast Ultrasound Image Classification Based on Multiple-Instance Learning
Breast Ultrasound Image Classification Based on Multiple-Instance Learning
复制标题
基于多实例学习的乳腺超声图像分类
DOI：
10.1007/s10278-012-9499-x
复制
发表时间：
2012-10-01
影响因子：
4.4
通讯作者：
Zhang, Yingtao
中科院分区：
文献类型：
--
作者：
Ding, Jianrui;Cheng, H. D.;Zhang, Yingtao
乳腺超声图像分割是一项非常困难的任务，由于图像质量差和斑点噪声。在本文中，局部特征提取的粗略分割区域的兴趣（ROI）被用来描述乳腺肿瘤。粗略分割的ROI被视为一个袋子。ROI的子区域被认为是袋子的实例。多示例学习（MIL）方法更适合于使用BUS图像进行乳腺肿瘤分类。然而，由于总线图像的复杂性，传统的MIL方法是不适用的。本文提出了一种新的MIL方法来解决这样的任务。首先，使用自组织映射将实例空间映射到概念空间。然后，我们使用每个袋子的实例在概念空间中的分布来构造袋子特征向量。最后，采用支持向量机对肿瘤进行分类。实验结果表明，该方法可以获得更好的性能：准确率为0.9107，受试者工作特征曲线下面积为0.96（p< 0.005）。
Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p< 0.005).