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Brain structures segmentation using optimum global and local weights on mixing active contours and neighboring constraints

Published: 26 October 2011 Publication History

Abstract

This paper presents a new method for segmenting multiple brain structures by using an optimized mixture of different Active Contour Models (ACMs). Prior constraints and structures' neighboring interaction are modelled for each structure. Prior information is also captured by a training process, in which structure's dependent local and global weights are calculated. The local weights regulate locally the combination of each term during the evolution, acting as an experienced balancer between image and prior information. The ideal proportion of relation between the mixture of different ACMs and the prior model is defined by the optimum global weights. As proof of concept, the method is applied on the very challenging task of segmenting hippocampus and amygdala structures.

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Cited By

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  • (2016)A review on brain structures segmentation in magnetic resonance imagingArtificial Intelligence in Medicine10.1016/j.artmed.2016.09.00173:C(45-69)Online publication date: 1-Oct-2016
  • (2014)Gradient-Based Reliability Maps for ACM-Based Segmentation of HippocampusIEEE Transactions on Biomedical Engineering10.1109/TBME.2013.229302361:4(1015-1026)Online publication date: Apr-2014

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  1. Brain structures segmentation using optimum global and local weights on mixing active contours and neighboring constraints

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      ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
      October 2011
      949 pages
      ISBN:9781450309134
      DOI:10.1145/2093698
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • Universitat Pompeu Fabra
      • IEEE
      • Technical University of Catalonia Spain: Technical University of Catalonia (UPC), Spain
      • River Publishers: River Publishers
      • CTTC: Technological Center for Telecommunications of Catalonia
      • CTIF: Kyranova Ltd, Center for TeleInFrastruktur

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 26 October 2011

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      Author Tags

      1. brain MRI segmentation
      2. gradient distribution on boundaries
      3. hippocampus-amygdala segmentation
      4. medical imaging
      5. prior knowledge
      6. region-based and gradient-based active contours

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      • Technical University of Catalonia Spain
      • River Publishers
      • CTTC
      • CTIF

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      Cited By

      View all
      • (2016)A review on brain structures segmentation in magnetic resonance imagingArtificial Intelligence in Medicine10.1016/j.artmed.2016.09.00173:C(45-69)Online publication date: 1-Oct-2016
      • (2014)Gradient-Based Reliability Maps for ACM-Based Segmentation of HippocampusIEEE Transactions on Biomedical Engineering10.1109/TBME.2013.229302361:4(1015-1026)Online publication date: Apr-2014

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