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A novel fuzzy clustering based system for medical image segmentation

机译:一种新颖的基于模糊聚类的医学图像分割系统

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摘要

Segmenting region of interest in medical images is challenging because medical image suffers from noise, degradation due to environment influence and low resolution due to devices, etc. In this paper, we have developed a novel idea based on weighted spatial kernel FCM clustering for segmenting region of interest in the medical images. Unlike traditional methods which ignore spatial information, we propose a new robust system that explores spatial information to remove uncertainty in identifying accurate region in the medical images. Furthermore, the proposed method estimates the weights for spatial information to derive precise membership function to segment the region based on Gaussian kernel as distance metric. We conducted experiments on standard datasets, namely MRI Brian image dataset and evaluated performance of the proposed method using recall, precision and f-measure. Experimental results reveal that, the proposed method performs better compared to existing methods.
机译:由于医学图像易受噪声,环境影响引起的退化以及设备造成的分辨率降低等问题,因此医学图像中感兴趣区域的分割具有挑战性。在本文中,我们提出了一种基于加权空间核FCM聚类的新颖思想来分割区域对医学图像感兴趣。与忽略空间信息的传统方法不同,我们提出了一种新的鲁棒系统,该系统可以探索空间信息以消除在标识医学图像中准确区域时的不确定性。此外,所提出的方法估计了空间信息的权重,以基于高斯核作为距离度量来推导精确的隶属度函数以对该区域进行分割。我们对标准数据集(即MRI Brian图像数据集)进行了实验,并使用召回率,精度和f量度评估了该方法的性能。实验结果表明,与现有方法相比,该方法具有更好的性能。

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