Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images. Based on a quantitative analysis over the mini-MIAS and PH² databases, the superiority of the proposed model in terms of segmentation accuracy, as compared with the ground truths, was confirmed. Furthermore, when using the proposed model, the processing time for image segmentation is lower than those when using other methods.

中文翻译：

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使用基于自适应权重的混合主动轮廓分割强度损坏的医学图像

分割精度是评估用于从图像中提取感兴趣对象的分割技术（例如活动轮廓模型）性能的重要标准。然而，分割精度会受到诸如强度不均匀性等图像伪影的影响，这使得提取强度不均匀的对象变得困难。为了解决这个问题，本文提出了一种基于混合区域的活动轮廓模型，用于非均匀图像的分割。提议的混合能量函数结合了局部和全局强度函数；结合的权重函数基于局部图像对比度进行参数化。包含这个权重函数可以平滑不同强度级别边界的轮廓，从而产生改进的分割。权重函数抑制虚假轮廓演化，并规范对象边界。与其他最先进的方法相比，所提出的方法在合成和真实图像上取得了更好的结果。基于对 mini-MIAS 和 PH 的定量分析^{2 个}数据库中，与地面实况相比，所提出的模型在分割精度方面的优越性得到了证实。此外，当使用所提出的模型时，图像分割的处理时间低于使用其他方法时的处理时间。