Selective segmentation aims to separate a subset of target objects or regions of interests in an image. It is widely used in medical image analysis for some specific tasks such as extracting anatomic organs or lesions. However, selective segmentation of medical images is usually challenged by their limited imaging quality. In this paper, we propose a two-phase selective segmentation method. The first phase is a preprocessing step, which aims to reduce influence of noise or cluttered background on segmentation. The second phase performs selective segmentation on the preprocessed image. For the first phase, we propose a new image smoothing model which can effectively reduce noise or intensity inhomogeneity inside objects while retain edges of the original image. Moreover, the proposed model has attractive mathematical and physical properties, in that it has one single optimal solution. For the second phase, we propose a modified Gout’s active contour method, which can obtain targeted objects more efficiently and accurately. Our main contribution is the new image smoothing model, which can effectively attenuate complicated background but preserve edges of targeted object. Extensive experiments on real medical images show that, our smoothing model can greatly facilitate the second phase, and our method can significantly improve some existing related methods in terms of either visual assessment or quantitative evaluation.

选择性分割旨在分离图像中目标对象或感兴趣区域的子集。它广泛用于医学图像分析，以完成某些特定任务，例如提取解剖器官或病变。然而，医学图像的选择性分割通常受到其有限的成像质量的挑战。在本文中，我们提出了一种两阶段选择性分割方法。第一阶段是预处理步骤，旨在减少噪声或杂乱背景对分割的影响。第二阶段对预处理后的图像进行选择性分割。对于第一阶段，我们提出了一种新的图像平滑模型，该模型可以在保留原始图像边缘的同时有效降低对象内部的噪声或强度不均匀性。此外，所提出的模型具有有吸引力的数学和物理特性，因为它只有一个最优解。对于第二阶段，我们提出了一种改进的 Gout 的主动轮廓方法，可以更高效、更准确地获取目标对象。我们的主要贡献是新的图像平滑模型，它可以有效地衰减复杂的背景但保留目标对象的边缘。对真实医学图像的大量实验表明，我们的平滑模型可以极大地促进第二阶段，并且我们的方法可以在视觉评估或定量评估方面显着改进一些现有的相关方法。可以有效衰减复杂的背景，同时保留目标对象的边缘。对真实医学图像的大量实验表明，我们的平滑模型可以极大地促进第二阶段，并且我们的方法可以在视觉评估或定量评估方面显着改进一些现有的相关方法。可以有效衰减复杂的背景，同时保留目标对象的边缘。对真实医学图像的大量实验表明，我们的平滑模型可以极大地促进第二阶段，并且我们的方法可以在视觉评估或定量评估方面显着改进一些现有的相关方法。