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Automatic hippocampus localization in histological images using PSO-based deformable models

Authors:

Roberto Ugolotti,

Stefano Cagnoni,

Mario Giacobini,

Ferdinando Di CuntoAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 487 - 494

https://doi.org/10.1145/2001858.2002038

Published: 12 July 2011 Publication History

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Automatic hippocampus localization in histological images using PSO-based deformable models

Pages 487 - 494

Abstract
References

Abstract

The Allen Brain Atlas (ABA) is a cellular-resolution, genome-wide map of gene expression in the mouse brain which allows users to compare gene expression patterns in neuroanatomical structures. The correct localization of the structures is the first step to carry on this comparison in an automatic way.

In this paper we present a completely automatic tool for the localization of the hippocampus that can be easily adapted also to other subcortical structures. This goal is achieved in two distinct phases.

The first phase, called "best reference slice selection", is performed by comparing the image of the brain with a reference Atlas provided by ABA using a two-step affine registration. By doing so the system is able to automatically find to which brain section the image corresponds and wherein the image the hippocampus is roughly located.

The second phase, the proper "hippocampus localization", is based on a method that combines Particle Swarm Optimization (PSO) and a novel technique inspired by Active Shape Models (ASMs). The hippocampus is found by adapting a deformable model derived statistically, in order to make it overlap with the hippocampus image.

Experiments on a test set of 120 images yielded a perfect or good localization in 89.2% of cases.

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Cited By

Ahmed SLee KJung H(2022)Robust Hippocampus Localization From Structured Magnetic Resonance Imaging Using Similarity Metric LearningIEEE Access10.1109/ACCESS.2021.313782410(7141-7152)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3137824
Vinoth Kumar BSabareeswaran SMadumitha G(2019)A Decennary Survey on Artificial Intelligence Methods for Image SegmentationAdvanced Engineering Optimization Through Intelligent Techniques10.1007/978-981-13-8196-6_27(291-311)Online publication date: 10-Jul-2019
https://doi.org/10.1007/978-981-13-8196-6_27
Mesejo PIbáñez ÓCordón ÓCagnoni S(2016)A survey on image segmentation using metaheuristic-based deformable modelsApplied Soft Computing10.1016/j.asoc.2016.03.00444:C(1-29)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.03.004
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Index Terms

Automatic hippocampus localization in histological images using PSO-based deformable models
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
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General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

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Published: 12 July 2011

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Ahmed SLee KJung H(2022)Robust Hippocampus Localization From Structured Magnetic Resonance Imaging Using Similarity Metric LearningIEEE Access10.1109/ACCESS.2021.313782410(7141-7152)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3137824
Vinoth Kumar BSabareeswaran SMadumitha G(2019)A Decennary Survey on Artificial Intelligence Methods for Image SegmentationAdvanced Engineering Optimization Through Intelligent Techniques10.1007/978-981-13-8196-6_27(291-311)Online publication date: 10-Jul-2019
https://doi.org/10.1007/978-981-13-8196-6_27
Mesejo PIbáñez ÓCordón ÓCagnoni S(2016)A survey on image segmentation using metaheuristic-based deformable modelsApplied Soft Computing10.1016/j.asoc.2016.03.00444:C(1-29)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.03.004
Cagnoni SArnold D(2014)Evolutionary image analysis and signal processingProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2605359(795-818)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2605359
Mesejo PCagnoni SCostalunga AValeriani DAlba E(2013)Segmentation of histological images using a metaheuristic-based level set approachProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2466808(1455-1462)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2466808
Mesejo PUgolotti RDi Cunto FGiacobini MCagnoni S(2013)Automatic hippocampus localization in histological images using Differential Evolution-based deformable modelsPattern Recognition Letters10.1016/j.patrec.2012.10.01234:3(299-307)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.patrec.2012.10.012
Cagnoni SCordón OMesejo PNashed YUgolotti RMoore J(2012)First results and future developments of the MIBISOC Project in the IBISlab of the university of parmaProceedings of the 14th annual conference companion on Genetic and evolutionary computation10.1145/2330784.2330865(509-516)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330784.2330865
Nashed YUgolotti RMesejo PCagnoni SMoore J(2012)libCudaOptimizeProceedings of the 14th annual conference companion on Genetic and evolutionary computation10.1145/2330784.2330803(117-124)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330784.2330803
Mesejo PUgolotti RCagnoni SDi Cunto FGiacobini M(2012)Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS.2012.6266318(1-4)Online publication date: Jun-2012
https://doi.org/10.1109/CBMS.2012.6266318

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