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MRI Brain Tumor Segmentation and Necrosis Detection using Adaptive Sobolev Snakes

1Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA.
2Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
3Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.
Publication Date:
Proc Soc Photo Opt Instrum Eng
Volume Number:
Proc Soc Photo Opt Instrum Eng. 2014 Mar 21;9034:903442.
PubMed ID:
Active contour, MRI analysis, necrosis detection, tumor segmentation
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Nakhmani A., Kikinis R., Tannenbaum A. MRI Brain Tumor Segmentation and Necrosis Detection using Adaptive Sobolev Snakes. Proc Soc Photo Opt Instrum Eng. 2014 Mar 21;9034:903442. PMID: 25302005. PMCID: PMC4187387.
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Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at different points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D diffusion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.

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