Multilevel segmentation and integrated bayesian model classification with an application to brain tumor segmentation

Jason J. Corso, Eitan Sharon, Alan Yuille

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

45 Scopus citations

Abstract

We present a new method for automatic segmentation of heterogeneous image data, which is very common in medical image analysis. The main contribution of the paper is a mathematical formulation for incorporating soft model assignments into the calculation of affinities, which are traditionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm. We apply the technique to the task of detecting and segmenting brain tumor and edema in multimodal MR volumes. Our results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of brain tumor.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings
Pages790-798
Number of pages9
DOIs
StatePublished - 2006
Event9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - Copenhagen, Denmark
Duration: 1 Oct 20066 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4191 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006
Country/TerritoryDenmark
CityCopenhagen
Period1/10/066/10/06

Fingerprint

Dive into the research topics of 'Multilevel segmentation and integrated bayesian model classification with an application to brain tumor segmentation'. Together they form a unique fingerprint.

Cite this