目的 医学图像分割结果可帮助医生进行预测、诊断及制定治疗方案。医学图像在采集过程中受多种因素影响，同一组织往往具有不同灰度，且伴有强噪声。现有的针对医学图像的分割方法，对图像的灰度分布描述不够充分，不足以为精确的分割图像信息，且抗噪性较差。为实现医学图像的精确分割，提出一种多描述子的活动轮廓（MDAC）模型。方法 首先，引入图像的熵，结合图像的局部均值和方差共同描述图像的灰度分布。其次，在贝叶斯框架下，引入灰度偏移因子，建立活动轮廓模型的能量泛函。最后，利用梯度下降流法得到水平集演化公式，演化的最后在完成分割的同时实现偏移场的矫正。结果 利用合成图像和心脏、血管和脑等医学图像进行了仿真实验。利用MDAC模型对加噪的灰度不均图像进行分割，结果显示，在完成精确分割的同时实现了纠偏。通过对比分割前后图像的灰度直方图，纠偏图像只包含对应两相的两个峰，且界限更加清晰；与经典分割算法进行对比，MDAC在视觉效果和定量分析中，分割效果最好，比LIC的分割精度提高了30%多。结论 实验结果表明，利用均值、方差和局部熵共同描述图像灰度分布，保证了算法的精度。局部熵的引入，在保证算法精度的同时，提高了算法的抗噪性。能泛中嵌入偏移因子，保证算法精确分割的同时实现偏移场纠正，进一步提高分割精度。

Objective Medical image segmentation results can help doctors predict, diagnose, and make a treatment plan. Medical images are affected by many factors in the process of collection in that the same tissue has different gray levels and is usually corrupted by strong noise. Existing medical images segmentation methods are undesirable because the description of the intensity distribution of the image is insufficient and the robustness to noise is poor. An active contour model with high accuracy and strong robustness to noise is proposed to obtain precise segmentation of medical images. Method First, entropy of an image is introduced with the local mean and the variance as the descriptor to represent the intensity distribution of the image. The entropy can reflect the richness of image information and measure the degree of heterogeneity of the image within segmentation regions. Great homogeneity of the partition corresponds to high entropy. The local entropy with mean and variance as the local image descriptors can obtain a high fitting degree for image intensity distribution. The value of the local entropy is not sensitive to a single noise pixel because it is determined by several pixels in a continuous domain. Therefore, local entropy has a certain filtering effect. Moreover, the normalization of intensity probability can smooth the noise, which can guarantee the robustness to noise of the proposed method especially when dealing with brain MR images with serious noises. Second, the bias field factor is introduced in the Bayesian framework to establish the energy function of the active contour model. The proposed method adopts the commonly used model to describe images with intensity inhomogeneity. The introduced bias field factor is spatially variant. The introduction of the bias factor can describe the inhomogeneous intensity well when model energy function is established. In the minimization of energy function, the bias factor is involved in the calculation, and the bias correction process is incorporated into the segmentation process to help improve segmentation accuracy. The evolution equation of the level set is obtained using the gradient descent method. At the end of evolution, the bias field correction is realized at the same time of the segmentation. We obtain the image segmentation result by minimizing this energy. Energy minimization is achieved by an iterative process. We minimize the energy with respect to each of its variables in each iteration given that the other three variables are updated in the previous iteration. Result Synthetic images and medical images, such as the heart, blood vessel, and brain, are used to perform simulation experiments. Experiment 1 verifies the effectiveness of the proposed algorithm. The MDAC model is used to segment intensity inhomogeneous images with noise, and results show that the bias-corrected image is achieved with accurate segmentation. Moreover, the gray histogram of the images before and after bias correction is calculated. The gray histogram of the image before bias correction has three peaks due to inhomogeneous intensity, and the boundaries are unclear. After bias correction, the gray histogram of the image has only two peaks because of the removal of the intensity inhomogeneity and the boundary is clear. In Experiment 2, the accuracy of the MDAC model and other two classic models, namely, LIC and LGDF models, is compared. The MDAC model has the best visual effects and quantitative analysis. Conclusion Experimental results show that the algorithm accuracy is guaranteed by mean, variance and local entropy to describe image intensity distribution. The introduction of local entropy ensures the accuracy of the algorithm and improves the robustness to the algorithm noise. In the energy function, the bias factor is embedded, which ensures that the algorithm performs bias correction at the same time of segmentation and further improves the accuracy of the algorithm. This method is extended to multiphase medical image segmentation. We attempt to employ partition entropy as data fitting energy to improve segmentation accuracy.