Multiresolution Multiscale Active Mask Segmentation of Fluorescence Microscope Images.
Multiresolution Multiscale Active Mask Segmentation of Fluorescence Microscope Images.
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DOI：
10.1117/12.825776
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Kovačević J
中科院分区：
文献类型：
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作者：
Srinivasa G;Fickus M;Kovačević J
我们提出了一个积极的掩模分割框架，结合了统计建模，平滑，速度和灵活性的传统方法的区域增长，多尺度，多分辨率和主动轮廓分别提供的优势。在这个框架的关键是一个范式的转变，从不断发展的轮廓在连续域中不断发展的多个面具在离散域。因此，主动掩模框架特别适合于分割数字图像。我们展示了在实践中使用的框架，通过分割的点状图案在荧光显微镜图像。实验表明，统计建模有助于多个掩模从随机的初始配置收敛到有意义的一个。这避免了需要一个涉及的初始化过程密切相关的大多数传统方法用于分割荧光显微镜图像。虽然我们提供了用于分割荧光显微镜图像的函数的数学细节，但这只是主动掩模框架的实例化。我们建议一些其他的框架实例分割不同类型的图像。
We propose an active mask segmentation framework that combines the advantages of statistical modeling, smoothing, speed and flexibility offered by the traditional methods of region-growing, multiscale, multiresolution and active contours respectively. At the crux of this framework is a paradigm shift from evolving contours in the continuous domain to evolving multiple masks in the discrete domain. Thus, the active mask framework is particularly suited to segment digital images. We demonstrate the use of the framework in practice through the segmentation of punctate patterns in fluorescence microscope images. Experiments reveal that statistical modeling helps the multiple masks converge from a random initial configuration to a meaningful one. This obviates the need for an involved initialization procedure germane to most of the traditional methods used to segment fluorescence microscope images. While we provide the mathematical details of the functions used to segment fluorescence microscope images, this is only an instantiation of the active mask framework. We suggest some other instantiations of the framework to segment different types of images.