Discriminative dimensionality reduction for patch-based label fusion

  • Authors
  • Sanroma G, Benkarim OM, Piella G, Wu G, Zhu X, Shen D, González Ballester MA
  • UPF authors
  • GONZALEZ BALLESTER, MIGUEL ANGEL; PIELLA FENOY, GEMA; BENKARIM ., MOHAMED OUALID; SANROMA GÜELL, GERARD;
  • Type
  • Scholarly articles
  • Journal títle
  • Lecture Notes in Computer Science / Artificial Intelligence
  • Publication year
  • 2016
  • Volume
  • 9487
  • Pages
  • 94-103
  • ISSN
  • 0302-9743
  • Publication State
  • Published
  • 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.
  • Complete citation
  • Sanroma G, Benkarim OM, Piella G, Wu G, Zhu X, Shen D, González Ballester MA. Discriminative dimensionality reduction for patch-based label fusion. Lecture Notes in Computer Science / Artificial Intelligence 2016; 9487( ): 94-103.
Bibliometric indicators
  • 6 times cited Scopus
  • 5 times cited WOS
  • Índex Scimago de 0.339(2016)