Discriminative dimensionality reduction for patch-based label fusion

Gerard Sanroma, Oualid M. Benkarim, Gemma Piella, Guorong Wu, Xiaofeng Zhu, Dinggang Shen, Miguel Ángel González Ballester

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

6 Citations (Scopus)

Abstract

In this last decade, multiple-atlas segmentation (MAS) has emerged as a promising technique for medical image segmentation. In MAS, a novel target image is segmented by fusing the label maps of a set of annotated images (or atlases), after spatial normalization. Weighted voting is a well-known label fusion strategy consisting of computing each target label as a weighted average of the atlas labels in a local neighborhood. The weights, denoting the local anatomical similarity of the candidate atlases, are often approximated using image-patch similarity measurements. Such an approach, known as patch-based label fusion (PBLF), may fail to discriminate the anatomically relevant patches in challenging regions with high label variability. In order to overcome this limitation we propose a supervised method that embeds the original image patches onto a space that emphasizes the appearance characteristics that are critical for a correct labeling, while supressing the irrelevant ones. We show that PBLF using the embedded patches compares favourably with state-of-the-art methods in brain MR image segmentation experiments.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages94-103
Number of pages10
Volume9487
ISBN (Print)9783319279282
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015 - Lille, France
Duration: 2015 Jul 112015 Jul 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9487
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015
CountryFrance
CityLille
Period15/7/1115/7/11

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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