Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm.
Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm.
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DOI：
10.1186/1752-0509-9-s5-s5
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发表时间：
2015
影响因子：
--
通讯作者：
Wang S
中科院分区：
文献类型：
--
作者：
Song H;Kang W;Zhang Q;Wang S
器官分割是计算机辅助诊断和病理检测的重要步骤。腹部计算机断层扫描（CT）序列中准确的肾脏分割是肾肿瘤消融手术计划和导航的必要和关键任务。然而，由于肾实质的强度值与邻近结构的强度值相似，因此在CT中进行肾脏分割是一项非常具有挑战性的工作。本文采用粗到精的方法从CT图像中分割肾脏，该方法分为粗分割和精分割两个阶段。粗分割基于核模糊c均值空间信息（SKFCM）算法，精分割采用改进的GrowCut （IGC）算法。SKFCM算法在模糊c均值聚类（FCM）算法中引入核函数和空间约束。IGC算法充分利用CT序列在空间上的连续性，可以自动生成种子标签，提高分割效率。在整个腹部CT图像数据集上进行的实验结果表明，该方法是准确有效的。该方法灵敏度为95.46%，特异度为99.82%，优于其他相关方法。该方法在肾脏分割中取得了较高的精度，大大减少了轮廓绘制所需的时间和人工。此外，该方法无需修改即可直接扩展到三维分割。
Organ segmentation is an important step in computer-aided diagnosis and pathology detection. Accurate kidney segmentation in abdominal computed tomography (CT) sequences is an essential and crucial task for surgical planning and navigation in kidney tumor ablation. However, kidney segmentation in CT is a substantially challenging work because the intensity values of kidney parenchyma are similar to those of adjacent structures. In this paper, a coarse-to-fine method was applied to segment kidney from CT images, which consists two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm and the refined segmentation is implemented with improved GrowCut (IGC) algorithm. The SKFCM algorithm introduces a kernel function and spatial constraint into fuzzy c-means clustering (FCM) algorithm. The IGC algorithm makes good use of the continuity of CT sequences in space which can automatically generate the seed labels and improve the efficiency of segmentation. The experimental results performed on the whole dataset of abdominal CT images have shown that the proposed method is accurate and efficient. The method provides a sensitivity of 95.46% with specificity of 99.82% and performs better than other related methods. Our method achieves high accuracy in kidney segmentation and considerably reduces the time and labor required for contour delineation. In addition, the method can be expanded to 3D segmentation directly without modification.