We discuss improved image reconstruction and segmentation in a framework we term model-controlled flooding (MCF). This extends the watershed transform for segmentation by allowing the integration of a priori information about image objects into flooding simulation processes. Modeling the initial seeding, region growing, and stopping rules of the watershed flooding process allows users to customize the simulation with user-defined or default model functions incorporating prior information. It also extends a more general class of transforms based on connected attribute filters by allowing the modification of connected components of a grayscale image, thus providing more flexibility in image reconstruction. MCF reconstruction defines images with desirable features for further segmentation using existing methods and can lead to substantial improvements. We demonstrate the MCF framework using a size transform that extends grayscale area opening and attribute thickening/thinning, and give examples from several areas: concealed object detection, speckle counting in biological single cell studies, and analyses of benchmark microscopic image data sets. MCF achieves benchmark error rates well below those reported in the recent literature and in comparison with other algorithms, while being easily adapted to new imaging contexts.

中文翻译：

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模型控制泛洪在图像重建和分割中的应用

我们在称为模型控制泛洪 (MCF) 的框架中讨论改进的图像重建和分割。这通过允许将有关图像对象的先验信息集成到洪水模拟过程中来扩展用于分割的分水岭变换。对流域洪水过程的初始播种、区域生长和停止规则进行建模允许用户使用包含先验信息的用户定义或默认模型函数来自定义模拟。它还通过允许修改灰度图像的连接组件，扩展了基于连接属性过滤器的更通用的变换类别，从而在图像重建中提供了更大的灵活性。MCF 重建定义了具有理想特征的图像，以便使用现有方法进行进一步分割，并且可以带来实质性的改进。我们使用扩展灰度区域开放和属性增厚/变薄的尺寸变换来演示 MCF 框架，并给出了几个领域的示例：隐藏对象检测、生物单细胞研究中的斑点计数以及基准显微图像数据集的分析。MCF 实现的基准错误率远低于最近文献中报告的错误率，并且与其他算法相比，同时很容易适应新的成像环境。生物单细胞研究中的斑点计数，以及基准显微图像数据集的分析。MCF 实现的基准错误率远低于最近文献中报告的错误率，并且与其他算法相比，同时很容易适应新的成像环境。生物单细胞研究中的斑点计数，以及基准显微图像数据集的分析。MCF 实现的基准错误率远低于最近文献中报告的错误率，并且与其他算法相比，同时很容易适应新的成像环境。