使用霍普菲尔德神经网络进行计算机化肿瘤边界检测。

Computerized tumor boundary detection using a Hopfield neural network.

作者信息

Zhu Y, Yan H

机构信息

Department of Electrical Engineering, University of Sydney, NSW, Australia.

出版信息

IEEE Trans Med Imaging. 1997 Feb;16(1):55-67. doi: 10.1109/42.552055.

DOI:10.1109/42.552055

PMID:9050408

Abstract

In this paper, we present a new approach for detection of brain tumor boundaries in medical images using a Hopfield neural network. The boundary detection problem is formulated as an optimization process that seeks the boundary points to minimize an energy functional based on an active contour model. A modified Hopfield network is constructed to solve the optimization problem. Taking advantage of the collective computational ability and energy convergence capability of the Hopfield network, our method produces the results comparable to those of standard "snakes"-based algorithms, but it requires less computing time. With the parallel processing potential of the Hopfield network, the proposed boundary detection can be implemented for real time processing. Experiments on different magnetic resonance imaging (MRI) data sets show the effectiveness of our approach.

摘要

在本文中，我们提出了一种使用霍普菲尔德神经网络检测医学图像中脑肿瘤边界的新方法。边界检测问题被表述为一个优化过程，该过程基于主动轮廓模型寻找边界点以最小化能量泛函。构建了一个改进的霍普菲尔德网络来解决该优化问题。利用霍普菲尔德网络的集体计算能力和能量收敛能力，我们的方法产生的结果与基于标准“蛇形”算法的结果相当，但所需计算时间更少。凭借霍普菲尔德网络的并行处理潜力，所提出的边界检测可用于实时处理。在不同的磁共振成像（MRI）数据集上进行的实验表明了我们方法的有效性。