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Self-Adaptive PCNN Based on the ACO Algorithm and its Application on Medical Image Segmentation

机译：基于ACO算法的自适应PCNN及其在医学图像分割中的应用

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Medical image segmentation plays a dominant role in medical image analysis and clinical research. As an effective method for image segmentation, pulse-coupled neural networks (PCNN) has its own limitation that the values of the parameters have a strong effect on its performance when it is used to segment the image. This paper proposed a new method for medical image segmentation using the self-adaptive PCNN model. In this method, we combined the searching capabilities of ant colony optimization (ACO) algorithm in the solution space with the biological characteristics of PCNN, to find the optimal values of PCNN's parameters for each input image. Moreover, the search process of the ACO algorithm was divided into the local searching and the global searching to accelerate the speed of the ASO's convergence. Based on the above work, a new automatic method for the image segmentation, namely ACO-PCNN, was presented. Lastly, four pairs of different MR medical images, including transaxial, sagittal, coronal sections and noisy medical images, were used to test and validate the performance of the proposed method. The experimental results illustrated that our method was accurate and effective to MRI medical images.
机译：医学图像分割在医学图像分析和临床研究中起着主导作用。作为一种有效的图像分割方法，脉冲耦合神经网络（PCNN）有其自身的局限性，即参数值在分割图像时会对其性能产生很大影响。提出了一种基于自适应PCNN模型的医学图像分割新方法。在这种方法中，我们将蚁群优化（ACO）算法在解空间中的搜索能力与PCNN的生物学特征相结合，从而为每个输入图像找到PCNN参数的最优值。此外，ACO算法的搜索过程分为局部搜索和全局搜索，以加快ASO的收敛速度。在上述工作的基础上，提出了一种新的自动图像分割方法，即ACO-PCNN。最后，使用四对不同的MR医学图像，包括经轴，矢状，冠状切面和嘈杂的医学图像，来测试和验证该方法的性能。实验结果表明我们的方法对MRI医学图像是准确有效的。

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