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Computerized tumour boundary detection using a Hopfield neural network

机译:使用Hopfield神经网络的计算机化肿瘤边界检测

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We present a new approach for detection of the brain tumour boundaries in medical images using a Hopfield network. The boundary detection problem is formulated as an optimization process that seeks the boundary points to minimize an energy functional based on the 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, results from the proposed method are comparable to those of standard snakes based algorithms, but with less computation time. Experiments on several magnetic resonance brain images show the effectiveness of our approach.
机译:我们提出了一种使用Hopfield网络检测医学图像中脑肿瘤边界的新方法。边界检测问题被配制成优化过程,其寻求基于主动轮廓模型最小化能量功能的边界点。构建修改过的Hopfield网络以解决优化问题。利用Hopfield网络的集体计算能力和能量收敛能力,所提出的方法的结果与基于标准蛇的算法相当,但计算时间较少。几个磁共振大脑图像的实验表明了我们方法的有效性。

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