Skip to Main content Skip to Navigation
Journal articles

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

Abstract : 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.
Complete list of metadata

Cited literature [5 references]  Display  Hide  Download

https://hal.inria.fr/hal-01221335
Contributor : Pablo Mesejo Santiago <>
Submitted on : Wednesday, October 28, 2015 - 2:23:32 PM
Last modification on : Friday, April 12, 2019 - 3:18:12 PM
Long-term archiving on: : Friday, January 29, 2016 - 1:10:21 PM

File

ELCVIA_SI_2014.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01221335, version 1

Collections

Citation

Pablo Mesejo. Automatic Segmentation of Anatomical Structures using Deformable Models and Bio-Inspired/Soft Computing. Electronic Letters on Computer Vision and Image Analysis, Computer Vision Center Press, 2014, 13 (2). ⟨hal-01221335⟩

Share

Metrics

Record views

177

Files downloads

211