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Journal of Imaging Informatics in Medicine

Published in:

01-10-2012

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

Authors: Jianrui Ding, H. D. Cheng, Jianhua Huang, Jiafeng Liu, Yingtao Zhang

Published in: Journal of Imaging Informatics in Medicine | Issue 5/2012

Abstract

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).

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Title: Breast Ultrasound Image Classification Based on Multiple-Instance Learning
Authors: Jianrui Ding
H. D. Cheng
Jianhua Huang
Jiafeng Liu
Yingtao Zhang
Publication date: 01-10-2012
Publisher: Springer-Verlag
Published in: Journal of Imaging Informatics in Medicine / Issue 5/2012
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-012-9499-x

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Breast Ultrasound Image Classification Based on Multiple-Instance Learning

Abstract

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