Medical image classification with multiple kernel learning
Hong Qi Wu, Hao Zhang, Chao Li · 2010
Nowadays, medical images are generated by hospitals and medical centers rapidly. The large volume of medical image data produces a strong need to effective medical image retrieval. The visual characteristic of medical image, such as modality, anatomical region etc., are important information and can be used to improve the retrieval process. Even though some of the information is contained in the DICOM headers, it has been reported that DICOM headers contain a relatively high rate of errors. And for on-line medical collection, these metadata can be lost when medical images are compressed. In this paper, we propose an algorithm for medical image classification according to their visual content. Our method uses multiple kernel learning (MKL) to combine different visual features, and learn the optimal mixing weights for each class adaptively. This method is evaluated on a medical image dataset with 1400 images, and the experimental results demonstrate the effectiveness of our method.