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.

Ultrasonic diffraction image of specimen informs its acoustic structure as X ray diffraction method for analysis of the crystal structure. This ultrasonic diffraction method has a feature that focused ultrasound beam is used and diffraction image is observed on focal plane. When the structure of specimen is perfectly periodic, its diffraction image produces symmetrical respect to origin, but the diffraction image of weak periodic structure such as living tissue has some asymmetricity. In this paper, the principle of ultrasonic diffraction method, and data processing for asymmetrical and scattered diffraction image caused by weak periodic structure are described. The results of diffraction image of plant tissue and animal tissue, and its discussion are also described. This method is expected to be useful in evaluation of acoustic structure such as living tissue and internal tissue of bone.