Multiple k-Nearest Neighbor Classifier and Its Application to Tissue Characterization of Coronary Plaque

In this paper we propose a novel classification method for the multiple k-nearest neighbor (MkNN) classifier and show its practical application to medical image processing. The proposed method performs fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided. The proposed MkNN classifier uses the continuity of the distribution of features of the same class not only in the feature space but also in the observation space. In order to validate the performance of the present method, it is applied to the tissue characterization problem of coronary plaque. The quantitative and qualitative validity of the proposed MkNN classifier have been confirmed by actual experiments. key words: acute coronary syndromes (ACS), coronary plaque tissue characterization, intravascular ultrasound (IVUS) method, multiple k-nearest neighbor (MkNN) classifier