改进的连续型最大流算法脑肿瘤磁核共振成像三维分割

Three-Dimensional Segmentation of Brain Tumors in Magnetic Resonance Imaging Based on Improved Continuous Max-Flow

针对脑肿瘤磁核共振成像(MRI)中噪声、低对比度、脑肿瘤边界模糊等原因造成脑肿瘤分割不足的问题,提出一种改进的连续型最大流算法脑肿瘤MRI三维分割方法。针对Flair、T1C和T2三种模态MRI图像使用中值滤波和快速模糊C均值聚类进行预处理得到预处理图像;按照大量实验统计确定的融合比例5:1:4(Flair、T1C和T2三种模态),对各预处理图像进行线性融合得到三维融合图像;采用快速模糊C均值算法对三维融合图像进行聚类得到三维欠分割图像;使用本文提出的算法对三维欠分割图像进行精准分割,即通过分析三维欠分割图像的结构特征和统计特征,提取参数实现改进的连续型最大流算法,去除散点后得到最终分割结果。实验表明,相对金标准的相似系数为0.90,正确率为0.94,召回率为0.86。使用本文提出算法进行脑肿瘤MRI三维分割能够自动、准确地分割出三维脑肿瘤区域,可以满足医学临床需要。

In order to solve the problem of insufficient segmentation of brain tumors in magnetic resonance imaging （MRI） caused by noise, poor contrast, and diffused boundaries of tumors, a three-dimensional （3D） segmentation algorithm for brain tumor MRI images based on the improved continuous max-flow is proposed in this paper. Firstly, three types of MIR images, Flair, T1C and T2, are pre-processed with median filtering and fast fuzzy C means clustering. Then, the pre-processed images are linearly fused in the ratio of 5：1：4 （Flair, TIC, and T2） which is statistically observed from a large amount of experiments. Next, the 3D fused image is clustered by the fast fuzzy C-means algorithm to obtain the 3D under-segmented image. Finally, the proposed improved continuous maxflow algorithm acts on the 3D under-segmented image to obtain the final segmentation result with scattering points removed according to the analysis of the structural features and statistical characteristics of the 3D under-segmented image. The experimental results show that the average Dice coefficient, precision, and recall of the proposed method relative to the gold standard is up to 0.90, 0.94, and 0.86, respectively. The proposed algorithm can realize the 3D segmentation of the target regions precisely and automatically to meet the clinical medicine requirement.