3D segmentation in MRI of brain tumors: preliminary results

Simon Vinitski, Carlos Gonzalez, Claudio Burnett, William Buchheit, Feroze Mohamed, Hector Ortega, Scott Faro

Research output: Contribution to journalConference articlepeer-review

Abstract

Tissue segmentation based on 2D and 3D feature map derived from high resolution MR images was performed in phantoms, normal humans and particularly those with brain tumors (four benign and two malignant). Three inputs: proton density, T2 - and, as a third, T1-weighted MRI, were utilized. Statistical and anisotropic diffusion filters were applied to the data and k-Nearest Neighborhood segmentation algorithm was utilized. The inclusion of T1 based images into segmentation produced dramatic improvement in tissue identification. Our technique utilizing all three inputs provided better segmentation (p < 0.001) than that based on any combination of two inputs. In benign brain tumors, we identified tumor volume prior to the injection of gadolinium-DTPA. In malignant tumors, up to four abnormal tissues were identified: 1) solid tumor core, 2) cyst, 3) edema in white matter and 4) edema in grey matter. Subsequent neurosurgery confirmed our model. These results encourage further investigation.

Original languageEnglish (US)
Pages (from-to)481-482
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume17
Issue number1
StatePublished - Dec 1 1995
EventProceedings of the 1995 IEEE Engineering in Medicine and Biology 17th Annual Conference and 21st Canadian Medical and Biological Engineering Conference. Part 2 (of 2) - Montreal, Can
Duration: Sep 20 1995Sep 23 1995

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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