Computer Science and Engineering Department, Chung-Ang University, Seoul, 06974, South Korea.
Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center (EMC), 3015, Rotterdam, The Netherlands.
Sci Rep. 2022 Sep 2;12(1):14947. doi: 10.1038/s41598-022-18708-5.
Level set models are suitable for processing topological changes in different regions of images while performing segmentation. Active contour models require an empirical setting for initial parameters, which is tedious for the end-user. This study proposes an incremental level set model with the automatic initialization of contours based on local and global fitting energies that enable it to capture image regions containing intensity corruption or other light artifacts. The region-based area and the region-based length terms use signed pressure force (SPF) to strengthen the balloon force. SPF helps to achieve a smooth version of the gradient descent flow in terms of energy minimization. The proposed model is tested on multiple synthetic and real images. Our model has four advantages: first, there is no need for the end user to initialize the parameters; instead, the model is self-initialized. Second, it is more accurate than other methods. Third, it shows lower computational complexity. Fourth, it does not depend on the starting position of the contour. Finally, we evaluated the performance of our model on microscopic cell images (Coelho et al., in: 2009 IEEE international symposium on biomedical imaging: from nano to macro, IEEE, 2009) to confirm that its performance is superior to that of other state-of-the-art models.
水平集模型适合在进行分割时处理图像不同区域的拓扑变化。活动轮廓模型需要对初始参数进行经验设置，这对最终用户来说很繁琐。本研究提出了一种基于局部和全局拟合能量的自动轮廓初始化的增量水平集模型，使其能够捕获包含强度失真或其他光伪影的图像区域。基于区域的面积和基于区域的长度项使用有符号压力力 (SPF) 来增强气球力。SPF 有助于在能量最小化方面实现梯度下降流的平滑版本。所提出的模型在多个合成和真实图像上进行了测试。我们的模型有四个优点：首先，无需用户初始化参数，模型可以自动初始化。其次，它比其他方法更准确。第三，它显示出更低的计算复杂度。第四，它不依赖于轮廓的起始位置。最后，我们在微观细胞图像（Coelho 等人，在：2009 IEEE 生物医学成像国际研讨会：从纳米到宏观，IEEE，2009 年）上评估了我们模型的性能，以确认其性能优于其他最先进的模型。
