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

7 Citations (Scopus)


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 publicationMachine Learning Meets Medical Imaging - 1st International Workshop, MLMMI 2015 Held in Conjunction with ICML 2015, Revised Selected Papers
EditorsKanwal K. Bhatia, Herve Lombaert
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319279282
Publication statusPublished - 2015
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)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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