A variant of learning vector quantizer based on the L/sub 2/ mean for segmentation of ultrasonic images
Constantine L. Kotropoulos, Ioannis Pitas, X. Magnisalis, M.G. Strintzis · 1993 IEEE International Symposium on Circuits and Systems · 2002
The segmentation of ultrasonic images using self-organizing neural networks (NN) is investigated. A modification of learning vector quantizer (called L/sub 2/ LVQ) is proposed so that the weight vectors of the output neurons correspond to the L/sub 2/ mean instead of the sample arithmetic mean of the input observations. The convergence in the mean and in the mean square of the proposed variant of LVQ is studied. Experimental results show that L/sub 2/ LVQ outperforms other segmentation techniques that employ thresholding a filtered ultrasonic image with respect to the probability of detection for the same probability of false alarm in all cases.>