Active volume models with probabilistic object boundary prediction module

Tian Shen, Yaoyao Zhu, Sharon Xiaolei Huang, Junzhou Huang, Dimitris Metaxas, Leon Axel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

We propose a novel Active Volume Model (AVM) which deforms in a free-form manner to minimize energy. Unlike Snakes and level-set active contours which only consider curves or surfaces, the AVM is a deforming object model that has both boundary and an interior area. When applied to object segmentation and tracking, the model alternates between two basic operations: deform according to current object prediction, and predict according to current appearance statistics of the model. The probabilistic object prediction module relies on the Bayesian Decision Rule to separate foreground (i.e. object represented by the model) and background. Optimization of the model is a natural extension of the Snakes model so that region information becomes part of the external forces. The AVM thus has the efficiency of Snakes while having adaptive region-based constraints. Segmentation results, validation, and comparison with GVF Snakes and level set methods are presented for experiments on noisy 2D/3D medical images.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
Pages331-341
Number of pages11
EditionPART 1
DOIs
StatePublished - Dec 1 2008
Event11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 - New York, NY, United States
Duration: Sep 6 2008Sep 10 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5241 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
CountryUnited States
CityNew York, NY
Period9/6/089/10/08

Fingerprint

Module
Snakes
Prediction
Model
Segmentation
Active Contours
Object
Level Set Method
Object Model
3D Image
Medical Image
Decision Rules
Natural Extension
Level Set
Alternate
Interior
Statistics
Minimise
Predict
Curve

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shen, T., Zhu, Y., Huang, S. X., Huang, J., Metaxas, D., & Axel, L. (2008). Active volume models with probabilistic object boundary prediction module. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings (PART 1 ed., pp. 331-341). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5241 LNCS, No. PART 1). https://doi.org/10.1007/978-3-540-85988-8_40
Shen, Tian ; Zhu, Yaoyao ; Huang, Sharon Xiaolei ; Huang, Junzhou ; Metaxas, Dimitris ; Axel, Leon. / Active volume models with probabilistic object boundary prediction module. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1. ed. 2008. pp. 331-341 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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Shen, T, Zhu, Y, Huang, SX, Huang, J, Metaxas, D & Axel, L 2008, Active volume models with probabilistic object boundary prediction module. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5241 LNCS, pp. 331-341, 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008, New York, NY, United States, 9/6/08. https://doi.org/10.1007/978-3-540-85988-8_40

Active volume models with probabilistic object boundary prediction module. / Shen, Tian; Zhu, Yaoyao; Huang, Sharon Xiaolei; Huang, Junzhou; Metaxas, Dimitris; Axel, Leon.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1. ed. 2008. p. 331-341 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5241 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - We propose a novel Active Volume Model (AVM) which deforms in a free-form manner to minimize energy. Unlike Snakes and level-set active contours which only consider curves or surfaces, the AVM is a deforming object model that has both boundary and an interior area. When applied to object segmentation and tracking, the model alternates between two basic operations: deform according to current object prediction, and predict according to current appearance statistics of the model. The probabilistic object prediction module relies on the Bayesian Decision Rule to separate foreground (i.e. object represented by the model) and background. Optimization of the model is a natural extension of the Snakes model so that region information becomes part of the external forces. The AVM thus has the efficiency of Snakes while having adaptive region-based constraints. Segmentation results, validation, and comparison with GVF Snakes and level set methods are presented for experiments on noisy 2D/3D medical images.

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M3 - Conference contribution

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Shen T, Zhu Y, Huang SX, Huang J, Metaxas D, Axel L. Active volume models with probabilistic object boundary prediction module. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1 ed. 2008. p. 331-341. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-540-85988-8_40