Medical Image Segmentation Based on Biomimetic Pattern Recognition

Abstract

A medical image segmentation algorithm based on biomimetic pattern recognition is proposed. First, psi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -neurons' weights are determined according to training samples and then multi-weight neuron networks are established. Second, the neuron networks are used to completely cover the samples' high-dimensional feature space. Finally, medical images are recognized and segmented based on the results of the optimal coverage of the samples. The experimental results show that the proposed method has higher accuracy, reliability and better generalization ability than the traditional medical image segmentation methods. Besides, from the cognitive respective, this algorithm emphasizes on "cognition", which can effectively integrate transcendental knowledge and obtain the desired object from medical images quickly and reliably, therefore it is highly intelligent.