Learning in Medical Image Databases
نویسنده
چکیده
In this paper we present several results obtained by experimenting with Bayesian minimum Maha-lanobis distance and k-nearest neighbor classiica-tion methods in a medical domain. The training set contains of four classes of diierent patholo-gies. Individual pathologies are represented in terms of their contour description, by a high-dimensional vector (the modal vector) abstracting their shape. We employ a divergence criterion to identify features with high discriminative power. The performance measurements suggest that the Bayes classiier outperforms the weighted k-nearest neighbor classi-er, a result which is not surprising considering the particular noisy structure of the training data set.
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