Automatic Annotation of X-ray Images: A Study on Attribute Selection
Devrim Unay, Octavian Soldea, Ahmet Ekin, Mujdat Cetin, Aytul Ercil
MCBR-CDS 2009 - Proc. of Medical Content-based Retrieval for Clinical Decision Support (MCBR-CDS) Workshop in conjunction with MICCAI’09, London - UK, 2009.
Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that need to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, the proposed solutions are still far from being su±ciently accurate for reallife implementations. In a previous work, performance of different feature types were investigated in a SVM-based learning framework for classification of X-Ray images into classes corresponding to body parts and local binary patterns were observed to outperform others. In this paper, we extend that work by exploring the effect of attribute selection on the classification performance. Our experiments show that principal component analysis based attribute selection manifests prediction values that are comparable to the baseline (all-features case) with considerably smaller subsets of original features, inducing lower processing times and reduced storage space.