Anatomy-Guided Brain Tumor Segmentation and Classification View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Bi Song , Chen-Rui Chou , Xiaojing Chen , Albert Huang , Ming-Chang Liu

ABSTRACT

In this paper, we consider the problem of fully automatic brain tumor segmentation in multimodal magnetic resonance images. In contrast to applying classification on entire volume data, which requires heavy load of both computation and memory, we propose a two-stage approach. We first normalize image intensity and segment the whole tumor by utilizing the anatomy structure information. By dilating the initial segmented tumor as the region of interest (ROI), we then employ the random forest classifier on the voxels, which lie in the ROI, for multi-class tumor segmentation. Followed by a novel pathology-guided refinement, some mislabels of random forest can be corrected. We report promising results obtained using BraTS 2015 training dataset. More... »

PAGES

162-170

Book

TITLE

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

ISBN

978-3-319-55523-2
978-3-319-55524-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-55524-9_16

DOI

http://dx.doi.org/10.1007/978-3-319-55524-9_16

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084775087


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