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Liver segmentation based on SKFCM and improved GrowCut for CT images

机译：基于SKFCM和改进的GrowCut的CT肝脏分割

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Accurate liver segmentation is an essential and crucial step for computer-aided liver disease diagnosis and surgical planning. In this paper, a new coarse-to-fine method is proposed to segment liver for abdominal computed tomography (CT) images. This hierarchical framework consists of rough segmentation and refined segmentation. The rough segmentation is implemented based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm and the refined segmentation is performed based on the proposed improved GrowCut (IGC) algorithm. The SKFCM algorithm introduces a kernel function and spatial constraint based on fuzzy c-means clustering (FCM) algorithm, which can reduce the effect of noise and improve the clustering ability. The IGC algorithm makes good use of the continuity of CT series in space which can automatically generate the seed labels and improve the efficiency of segmentation. The proposed method was applied to segment the liver for the whole dataset of abdominal CT images. The performance evaluation of segmentation results shows that the proposed liver segmentation method is accurate and efficient. Experimental results have been shown visually and achieve reasonable consistency.
机译：准确的肝脏分割是计算机辅助肝病诊断和手术计划的关键和至关重要的步骤。在本文中，提出了一种新的从粗到细的方法来分割腹部的腹部计算机断层扫描（CT）图像的肝脏。此层次结构框架由粗略细分和精细细分组成。基于带有空间信息的核模糊C均值算法（SKFCM）实现粗分割，并基于提出的改进的GrowCut（IGC）算法进行细化分割。 SKFCM算法引入了基于模糊c均值聚类（FCM）算法的核函数和空间约束，可以减少噪声影响并提高聚类能力。 IGC算法充分利用了空间CT序列的连续性，可以自动生成种子标签并提高分割效率。该方法被用于分割腹部CT图像的整个数据集的肝脏。分割结果的性能评估表明，所提出的肝脏分割方法准确有效。实验结果已经直观地显示出来并达到了合理的一致性。

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