Report on the Annotation Task in ImageCLEFmed 2005

In the medical image annotation task we have mainly explored ways to use different image features to achieve robust classification performance, including both global features and regional blob features. Experimental results show that using a combination of the blob region feature and three low resolution pixel maps (gray level, texture and contrast) can achieve the highest recognition accuracy. All these features are normalized and stacked to form a one-dimension feature vector as inputs of classifiers. In our experiments Supporting Vector Machines (SVM) with RBF (radial basis functions) kernels are used for the classification task, trained over a subset of 9000 given medical training images. Our proposed method has achieved a recognition rate of 89% over a subset of the training images which were not used in the SVM training. According to the evaluation result from the imageCLEF05 organizers, our method has achieved a recognition rate of about 80% over the 1000 testing images.