Automatic Segmentation of Anatomical Structures using Deformable Models and Bio-Inspired/Soft Computing

ELCVIA Electronic Letters on Computer Vision and Image Analysis

Pablo Mesejo¹ 

关键词: Biomedical Imaging;    Soft Computing;    Metaheuristics;    Deformable Models;    Ensemble Classifiers;    Level Set Method;    Active Shape Models;    Hippocampus;    Differential Evolution;    Multimodal Optimization;   

DOI  :  

学科分类：计算机科学（综合）

来源: ELCVIA

【 摘 要 】

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.

【 授权许可】

Unknown