一种基于神经网络的医学数据场体分割新算法

直接体分割是三维医学数据场处理的难点和研究热点之一.利用BP神经网络的聚类技术,提出了一种新的三维医学数据场的聚类分割算法.首先,根据医学数据的物理意义,对数据场进行预处理,以加快后继处理速度;随后分析推导了基于神经网络的聚类分割算法,并提出了若干加速算法收敛速度的方法.实验结果表明该算法不仅能够提高三维医学组织的聚类分割精度,而且能够模型的处理速度.

A New Volume Segmentation Algorithm for Medical Data Field Based on Neural Network

Direct 3D volume segmentation is one of the difficult and hot research fields in 3D medical data field processing.Using the clustering techniques of neural network,a new clustering segmentation algorithm is presented.Firstly,According to the physical means of the medical data,the data field is preprocessed to speed up succeed processing.Secondly,the paper deduces and analyzes the clustering and segmentation algorithm and presents some methods to increase the process speed.Finally,the experimental results show that the algorithm has high accuracy when used to segment 3D medical tissue and can improve process speed greatly.