Improving the annotation accuracy of medical images in ImageCLEFmed2005 using K-Nearest Neighbor (kNN) classifier

Abstract

Content-based image retrieval systems offer solution to store and search the ever increasing amount of digital images currently in existence. These systems retrieve and extract the images based on low level features, such as color, texture and shape. However, these visual features did not enable users to request images based on semantic meanings. Semantic retrieval is of highly importance in various domains and in particular the medical domain that contain images from various medical devices such as MRI and X-ray. Image annotation or classification systems can be considered as a solution for the limitations of existing CBIR systems. In this study, it was proposed a new approach for image classification using multi-level features and machine learning techniques, particularly the K-Nearest Neighbor (kNN) classifier. We experimented the proposed approach on 9000 images available from the ImageCLEFmed2005 dataset. Principle Component Analysis (PCA) was performed to reduce the feature vectors. The accuracy results achieved 89.32% and 92.99% for the respective 80 and 90% of training images. The results show improvement as compared to previous studies for the same dataset.