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首页> 外文期刊>Electronic Letters on Computer Vision and Image Analysis: ELCVIA >Automatic Segmentation of Anatomical Structures using Deformable Models and Bio-Inspired/Soft Computing
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Automatic Segmentation of Anatomical Structures using Deformable Models and Bio-Inspired/Soft Computing

机译：使用可变形模型和生物启发/软计算自动分割解剖结构

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This PhD dissertation is focused on the development of algorithms for the automatic segmentation of anatomical structures in biomedical images, usually the hippocampus in histological images from the mouse brain. Such algorithms are based on computer vision techniques and artificial intelligence methods. More precisely, on the one hand, we take advantage of deformable models to segment the anatomical structure under consideration, using prior knowledge from different sources, and to embed the segmentation into an optimization framework. On the other hand, metaheuristics and classifiers can be used to perform the optimization of the target function defined by the shape model (as well as to automatically tune the system parameters), and to refine the results obtained by the segmentation process, respectively. Three new different methods, with their corresponding advantages and disadvantages, are described and tested. A broad theoretical discussion, together with an extensive introduction to the state of the art, has also been included to provide an overview necessary for understanding the developed methods.
机译：本博士论文致力于生物医学图像中解剖结构自动分割算法的开发，通常是来自小鼠大脑组织学图像中的海马体。这样的算法基于计算机视觉技术和人工智能方法。更准确地说，一方面，我们利用可变形模型利用来自不同来源的先验知识对考虑中的解剖结构进行分割，并将分割嵌入到优化框架中。另一方面，元启发式算法和分类器可用于对形状模型定义的目标函数进行优化（以及自动调整系统参数），并细化通过分割过程获得的结果。描述并测试了三种新的不同方法，具有各自的优点和缺点。还包括了广泛的理论讨论，以及对现有技术的广泛介绍，以提供理解所开发方法所必需的概述。

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