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Unsupervised Segmentation of Medical Image Based on FCM and Mutual Information

机译:基于FCM和互信息的医学图像无监督分割

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In the scope of medical image processing, segmentation is important and difficult.This paper presents a novel algorithm for segmentation of medical image.Our algorithm is formulated by combining the fuzzy c-means clustering (FCM)algorithm with the mutual information (MI) technique.The initial threshold can be chosen using FCM algorithm,and in the iteration process,an optimal threshold will be determined by maximizing the MI between the original volume and the thresholded volume.We evaluate the effectiveness of the proposed approach by applying it to the medical images, including magnetic resonance imaging (MRI),microphotographic image.The experimental results indicate that the proposed method has not only visually better or comparable segmentation effect but also,more favorably,removal ability for noise.
机译:在医学图像处理领域,分割是重要而困难的。本文提出了一种新的医学图像分割算法。我们的算法是将模糊c均值聚类(FCM)算法与互信息(MI)技术相结合提出的。可以使用FCM算法选择初始阈值,并且在迭代过程中,将通过最大化原始体积和阈值体积之间的MI来确定最佳阈值。实验结果表明,该方法不仅具有较好的视觉分割效果或相当的分割效果,而且具有较好的去噪能力。

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