Lin J S, Cheng K S, Mao C W
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, 70101, Republic of China.
Comput Biomed Res. 1996 Aug;29(4):314-26. doi: 10.1006/cbmr.1996.0023.
Segmentation (tissue classification) of the medical images obtained from Magnetic resonance (MR) images is a primary step in most applications of computer vision to medical image analysis. This paper describes a penalized fuzzy competitive learning network designed to segment multispectral MR spin echo images. The proposed approach is a new unsupervised and winner-takes-all scheme based on a neural network using the penalized fuzzy clustering technique. Its implementation consists of the combination of a competitive learning network and penalized fuzzy clustering methods in order to make parallel implementation feasible. The penalized fuzzy competitive learning network could provide an acceptable result for medical image segmentation in parallel processing using the hardware implementation. The experimental results show that a promising solution can be obtained using the penalized fuzzy competitive learning neural network based on least squares criteria.
从磁共振（MR）图像中获取的医学图像的分割（组织分类）是计算机视觉在医学图像分析的大多数应用中的首要步骤。本文描述了一种用于分割多光谱MR自旋回波图像的惩罚模糊竞争学习网络。所提出的方法是一种基于神经网络的新的无监督且胜者全得方案，它使用了惩罚模糊聚类技术。其实现包括竞争学习网络和惩罚模糊聚类方法的结合，以便使并行实现可行。惩罚模糊竞争学习网络可以通过硬件实现为并行处理中的医学图像分割提供可接受的结果。实验结果表明，基于最小二乘准则的惩罚模糊竞争学习神经网络可以获得有前景的解决方案。
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