基于监督下降方法的左心室超声图像分割

针对基于表观模型的图像分割算法在特征点迭代定位过程中计算量过大、对非线性局部特征的优化较为困难等问题，采用一种基于监督学习的梯度下降算法，建立4层多分辨率金字塔模型，并使用一种基于巴氏系数的特征提取函数（B-SIFT）替代原方法中的尺度不变特征变换（SIFT）特征，对左心室心内膜及心外膜进行特征点定位。首先对训练集进行归一化处理，统一经食道超声心动图像（TEE）的尺度；然后建立基于多分辨率金字塔和B-SIFT特征的监督下降模型，得到特征点趋近于真实值的梯度下降方向序列；最后将得到的方向序列作用于测试集中，得到最终的左心室分割结果。将该方法与传统监督下降方法进行对比，其得到的分割平均误差相比传统监督下降方法降低了47%，迭代得到的最终值相对单一尺度的梯度下降算法更加逼近真实值。

Left ventricle segmentation in transesophageal echocardiography based on supervised descent method

The image segmentation method based on appearance-model has high computational complexity in iterative positioning feature points, and it is difficult to optimize the nonlinear local feature. To solve these above problems and locate feature points of left ventricular endocardium and epicardium, a gradient decent algorithm based on supervised learning was proposed, a multi-resolution pyramid model of 4 levels was built, and a new feature extraction function based on Bhattacharyya coefficient, namely B-SIFT, was used to replace the Scale Invariant Feature Transform (SIFT) feature in the original method. Firstly, the training set images were normalized to unify the size of each TransEsophageal Echocardiography (TEE). Then the supervised descent model based on B-SIFT and multi-resolution pyramid was built to get a gradient descent direction sequence that approaches the actual values. Finally, the learned direction sequence was applied to the test set to obtain the segmentation results of left ventricular. The experimental results show that compared with the traditional gradient decent method based on supervised learning, the average segmentation error of the proposed method is reduced by 47%, and the iteration results are more closer to the actual values compared with the single-scale method.