基于二维自动主动形状模型的椎间盘核磁共振图像分割算法

Segmentation algorithm of intervertebral disc magnetic resonance images based on two-dimensional automatic active shape model

针对椎间盘手动建模主观耗时以及现有分割方法不够准确的问题,提出了一种二维自动主动形状模型(2D-AASM)方法,由基于最小描述长度的椎间盘自动统计形状建模、二维局部梯度建模和分割三部分组成。将25组脊柱核磁共振图像(MRI)的椎间盘专家分割结果作为训练集,采用基于最小描述长度的方法确定点对应关系,建立椎间盘T4-5的统计形状模型和二维局部梯度模型,生成形状模型的方差和目标函数值均小于手工和弧长参数方法。模型建立后,通过3组脊柱MRI数据测试提出的分割方法,与传统主动形状模型(ASM)和加入一维局部梯度模型的ASM方法相比,其分割结果具有更高的戴斯系数值,更低的过分割率和欠分割率。实验结果表明,所提方法建立的模型更准确,分割结果更精确。

In response to the issue that the intervertebral disk manual modeling was time-consuming and subjective, and the existing segmentation method was not accurate enough, a new method named two-diememsional Automatic Active Shape Model （2D-AASM） was proposed. It included three parts： automatic statistical shape modeling of intervertebral disk based on minimum description length, 2D local gradient modeling and segmentation. Adopting the manual segmentation results of 25 sets of spinal MR images as the training set, the study used minimum description length method to determine the point correspondence, built statistical shape model and 2D local gradient model for intervertebral disk T4-5. The generated shape model had lower variance and the objective function value than the manual and arc length parameter method. After the model was built, three sets of Magnetic Resonance hnage （MRI） images were used to test the proposed method. Compared with the traditional ASM and 1D-ASM, the segmentation result of the proposed method had a higher Dice coefficient and lower over- segmentation and under-segmentation rate. The experiment resuhs indicate that the proposed method generates a better model and more accurate segmentation result.