# Difference between revisions of "Projects:KnowledgeBasedBayesianSegmentation"

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− | Back to [[NA- | + | Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:Stony Brook|Stony Brook University Algorithms]], [[Engineering:Kitware|Kitware Engineering]] |

− | + | __NOTOC__ | |

− | + | = Knowledge Based Bayesian Segmentation = | |

This ITK filter is a segmentation algorithm which utilizes Bayes's Rule along with an affine-invarient anisotropic smoothing filter. | This ITK filter is a segmentation algorithm which utilizes Bayes's Rule along with an affine-invarient anisotropic smoothing filter. | ||

− | + | = Description = | |

''Use Case'' | ''Use Case'' | ||

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''Data'' | ''Data'' | ||

− | We are | + | We have applied this algorithm to 20 normal brain MRI data-sets. We used publicly available data-sets from |

+ | the Internet Brain Segmentation Repository (IBSR) offered by the Massachusetts General Hospital, Center for | ||

+ | Morphometric Analysis. The IBSR data-sets are T1-weighted, 3D coronal brain scans after having been | ||

+ | positionally normalized. Manual expert segmentations for these data-sets are publicly available and represent | ||

+ | the ground truth used in this work. | ||

''Algorithm'' | ''Algorithm'' | ||

+ | |||

+ | This algorithm can be cast in either a static or dynamic framework. In the static framework, the following is the algorithm: | ||

# The user sets the number of distinct classes for segmentation: 'N' | # The user sets the number of distinct classes for segmentation: 'N' | ||

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# Smooth the posterior images for 'm' iterations using an affine-invarient anisotropic smoothing filter and renormalize after each iteration (default, m = 5) <br /> | # Smooth the posterior images for 'm' iterations using an affine-invarient anisotropic smoothing filter and renormalize after each iteration (default, m = 5) <br /> | ||

# Apply maximum a posteriori rule to apply labeling and finalize segmentation | # Apply maximum a posteriori rule to apply labeling and finalize segmentation | ||

+ | |||

+ | In the dynamic framework, the following image depicts the adaptation of the static framework to the dynamic formulation: | ||

+ | |||

+ | [[Image:Flowchart-classification.png| Dynamic Tissue Tracking Algorithm | center]] | ||

+ | |||

+ | <br/> | ||

''The ITK filter design'' | ''The ITK filter design'' | ||

− | + | <br/> | |

+ | |||

+ | [[Image:Flowchart.png| Flowchart]] | ||

''Some Results'' | ''Some Results'' | ||

− | * [[Image: | + | * [[Image:Plot white.png | White Matter Performance on the 20 ISBR datasets | 600px]] WM Algorithm Comparisons |

− | * [[Image: | + | * [[Image:Plot gray.png | Gray Matter Performance on the 20 ISBR datasets | 600px]] GM Algorithm Comparisons |

− | * [[Image: | + | * [[Image:Fig67.png | Visual Results | 600px]] Visual Results on ISBR data |

''Project Status'' | ''Project Status'' | ||

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* The working ITK code has been committed to the [http://www.na-mic.org:8000/svn/NAMICSandBox/BayesianSegmentationModule/ SandBox] | * The working ITK code has been committed to the [http://www.na-mic.org:8000/svn/NAMICSandBox/BayesianSegmentationModule/ SandBox] | ||

− | + | = Key Investigators = | |

− | * | + | * Georgia Tech Algorithms: John Melonakos, Yi Gao, Allen Tannenbaum |

− | * | + | * Kitware Engineering: Luis Ibanez, Karthik Krishnan |

− | + | = Publications = | |

− | * [ | + | ''In Print'' |

− | + | * [http://www.na-mic.org/publications/pages/display?search=KnowledgeBasedBayesianSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database on Knowledge-Based Bayesian Segmentation] | |

− | |||

− | |||

− | |||

− | |||

− | + | [[Category: Segmentation]] [[Category:MRI]] [[Category:Slicer]] | |

− |

## Latest revision as of 00:56, 16 November 2013

Home < Projects:KnowledgeBasedBayesianSegmentationBack to NA-MIC Collaborations, Stony Brook University Algorithms, Kitware Engineering

# Knowledge Based Bayesian Segmentation

This ITK filter is a segmentation algorithm which utilizes Bayes's Rule along with an affine-invarient anisotropic smoothing filter.

# Description

*Use Case*

I'd like to segment a volume or sub-volume into 'N' classes in a very general manner. I will provide the data and the number of classes that I expect and the algorithm will output a labelmap with 'N' classes.

*Data*

We have applied this algorithm to 20 normal brain MRI data-sets. We used publicly available data-sets from the Internet Brain Segmentation Repository (IBSR) offered by the Massachusetts General Hospital, Center for Morphometric Analysis. The IBSR data-sets are T1-weighted, 3D coronal brain scans after having been positionally normalized. Manual expert segmentations for these data-sets are publicly available and represent the ground truth used in this work.

*Algorithm*

This algorithm can be cast in either a static or dynamic framework. In the static framework, the following is the algorithm:

- The user sets the number of distinct classes for segmentation: 'N'
- Generate 'N' prior images (default, 'N' uniform prior images)
- Generate 'N' statistical distributions (default, 'N' normal distributions)
- Generate 'N' membership images by applying the statistical distributions to the raw data
- Generate 'N' posterior images by applying Bayes' rule to the prior and membership images
- Smooth the posterior images for 'm' iterations using an affine-invarient anisotropic smoothing filter and renormalize after each iteration (default, m = 5)
- Apply maximum a posteriori rule to apply labeling and finalize segmentation

In the dynamic framework, the following image depicts the adaptation of the static framework to the dynamic formulation:

*The ITK filter design*

*Some Results*

*Project Status*

- Fully incorporated into itkBayesianClassificationImageFilter and itkBayesianClassificationInitializationImageFilter in ITK CVS.
- Fully wrapped in VTK for use in Slicer.
- The working ITK code has been committed to the SandBox

# Key Investigators

- Georgia Tech Algorithms: John Melonakos, Yi Gao, Allen Tannenbaum
- Kitware Engineering: Luis Ibanez, Karthik Krishnan

# Publications

*In Print*