Comparison of global features for categorization of medical images

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Abstract

We present an evaluation of methods for the automatic categorization of medical images. The properties of medical images render some otherwise very successful discriminate features for images (e.g. color) inapplicable. Therefore, we evaluate several feature types: texture, structure, and down-scaled representations. The classification is done using a nearest neighbor classifier with various distance measures as well as the automatic combination of classifier results. A corpus of 6,335 images selected arbitrarily from the clinical routine was encoded using a multi-axial, mono-hierarchical code. The reference categorization was done by experienced radiologists familiar with the code. The code's hierarchy allows the analysis of the automatic categorization performance (depending on the features and the classifier used) at different levels of differentiation. Experiments were done for 54 and 57 categories or 70 and 81 categories focussing on radiographs only or for all images, respectively. A maximum classification accuracy of 86% was obtained using the winner-takes-all rule and a one nearest neighbor classifier. Accuracy is increased to 93% and 95% if the correct category is only required to be within the 5 or 10 best matches, respectively. In this case, the best rate of 98% is obtained. This is sufficient for most applications in content-based image retrieval.

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