RSF模型的优化及其在MRI脑肿瘤分割中的应用

RSF Model Optimization and Its Application to Brain Tumor Segmentation in MRI

磁共振成像(MRI)具有图像模糊,灰度不均等特点,其分割问题一直都是研究的热点和难点。可变区域拟合(RSF)能量模型是一种较新的区域活动轮廓模型,可用于灰度不均匀图像的分割。然而,RSF模型设定的水平集函数(LSF)不适合初始轮廓内外灰度分布不同的环境,应用于整体灰度环境复杂的脑肿瘤MRI图像时,通常得不到理想的分割结果。构建新的LSF,并辅以mean shift平滑算法可使其更适用于肿瘤图像的分割,使新模型具有更好的收敛性和目标指向性。利用优化后的模型进行一系列实验,其结果表明:该算法鲁棒性强,可以快速、准确地分割出MRI图像中的脑肿瘤,具有显著的临床意义。

Magnetic resonance imaging （MRI） is usually obscure and non-uniform in gray, and the tumors inside are poorly circumscribed, hence the automatic tumor segmentation in MRI is very difficult. Region-scalable fitting （RSF） energy model is a new segmentation approach for some uneven grayseale images. However, the level set for- mulation （LSF） of RSF model is not suitable for the environment with different grey level distribution inside and out- side the intial contour, and the complex intensity environment of MRI always makes it hard to get ideal segmentation results. Therefore, we improved the model by a new LSF and combined it with the mean shift method, which can be helpful for tumor segmentation and has better convergence and target direction. The proposed method has been uti- lized in a series of studies for real MRI images, segmentations for brain tumors in MRI, which and the results showed that it could realize fast, accurate and robust has great clinical significance.