Segmenting brain tumors using alignment-based features

  title={Segmenting brain tumors using alignment-based features},
  author={Mark W. Schmidt and Ilya Levner and Russell Greiner and Albert Murtha and Aalo Bistritz},
  journal={Fourth International Conference on Machine Learning and Applications (ICMLA'05)},
  pages={6 pp.-}
Detecting and segmenting brain tumors in magnetic resonance images (MRI) is an important but time-consuming task performed by medical experts. Automating this process is a challenging task due to the often high degree of intensity and textural similarity between normal areas and tumor areas. Several recent projects have explored ways to use an aligned spatial 'template' image to incorporate spatial anatomic information about the brain, but it is not obvious what types of aligned information… CONTINUE READING
Highly Cited
This paper has 169 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Explore Further: Topics Discussed in This Paper


Publications citing this paper.
Showing 1-10 of 47 extracted citations

Within-brain classification for brain tumor segmentation

International Journal of Computer Assisted Radiology and Surgery • 2015
View 3 Excerpts
Highly Influenced

A survey of MRI-based medical image analysis for brain tumor studies.

Physics in medicine and biology • 2013
View 3 Excerpts
Highly Influenced

A comprehensive study of features used for brian tumor detection and segmentation from Mr images

2017 Innovations in Power and Advanced Computing Technologies (i-PACT) • 2017
View 2 Excerpts

Brain tumor segmentation with Deep Neural Networks

Medical Image Analysis • 2017
View 1 Excerpt

A two phase segmentation algorithm for MRI brain tumor extraction

2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) • 2016
View 1 Excerpt

169 Citations

Citations per Year
Semantic Scholar estimates that this publication has 169 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 20 references

A method for standardizing mr intensities between slices and volumes

M. Schmidt
Technical report, University of Alberta • 2005
View 3 Excerpts
Highly Influenced

Joachims . Making large - scale svm learning practical

B. Scholkopf, C. J. C. Burges, A. J. Smola
Computer Vision : A Modern Approach • 2003