Descombes Xavier
Université Côte d'Azur, INRIA CRI-SAM, I3S, iBV, France.
Methods. 2017 Feb 15;115:2-8. doi: 10.1016/j.ymeth.2016.09.009. Epub 2016 Sep 21.
The marked point process framework has been successfully developed in the field of image analysis to detect a configuration of predefined objects. The goal of this paper is to show how it can be particularly applied to biological imagery. We present a simple model that shows how some of the challenges specific to biological data are well addressed by the methodology. We further describe an extension to this first model to address other challenges due, for example, to the shape variability in biological material. We finally show results that illustrate the MPP framework using the "simcep" algorithm for simulating populations of cells.
标记点过程框架已在图像分析领域成功开发，用于检测预定义对象的配置。本文的目的是展示它如何特别应用于生物图像。我们提出了一个简单的模型，展示了该方法如何很好地应对生物数据特有的一些挑战。我们进一步描述了对第一个模型的扩展，以应对其他挑战，例如由于生物材料形状的变异性所导致的挑战。我们最终展示了使用“simcep”算法模拟细胞群体来说明MPP框架的结果。
