GRNN을 이용한 대역 분할된 심장조영상의 움직임 예측기법
Motion Prediction Technique of Subbanded Cardio-Angiography using GRNN
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Abstract
Medical images with high resolution are coded to be archived and communicated in PACS. In this paper, a new nonlinear predictor using neural network(GRNN) is proposed for the subband coding of Cardio-Angiography. The performance of a proposed nonlinear predictor is compared with BMA(Block Match Algorithm), the most conventional motion estimation technique. As a result, the nonlinear predictor using GRNN can predict well more 2-3dB than BMA. Specially, because of having a clustering process and smoothing noise signals, this predictor well preserves edges in frames after predicting the subband signal. This result is important with respest of human visual system.