Combining Local Features and Multi-instance Learning for Ultrasound Image Classiflcation
Jianrui Ding, Jianhua Huang, Jiafeng Liu, Yingtao Zhang · 2013
The method to describe ultrasound images using global features has some limitation. And it has a high cost to manually annotate the region of interest (ROI). To solve the above problems, local features are used to describe ultrasound images. A multi-instance learning method for ultrasound image classiflcation is proposed. The ROI is roughly located and local features are extracted. The ROI is considered as a bag which is composed of local features. The self-organizing map (SOM) method is used for vector quantization and the bag of words method is used to map instance features to bag features. Then the classical support vector machine is used to classify the bags. The method proposed by this paper is tested by clinical ultrasound images. The results show that the method has a good generalization ability and has a high