SPAM recognition, Markovian model

The SPAM sulci recognition is a newer alternative to the older recognition process. Several variants of it are available under the SPAM recognition process. This one is the "Markovian" method, which should take place after a "global registration".

Description

SPAM recognition is based on a probabilistic model which provides for any 3D position the probability of presence of every sulcus. Several variants and additions may also be taken into account (conjoint registration, additional prior contstraints, mix with the older neural network-based recognition, etc.) in a bayesian probabilistic framework.

The current version is a second step, and must take place after a global registration step. It performs "Markovian labeling", and optimizes the sulci, taking into account both the SPAM maps and distance information between neighbouring sulcal segments.

Other methods can be accessed via the general process SPAM recognition.

For a more precise description of the method, see:

• M. Perrot, D. Rivière, and J.-F. Mangin. Cortical sulci recognition and spatial normalization. Medical Image Analysis, 15(4):529-550, 2011.
• M. Perrot, D. Rivière, A. Tucholka, and J.-F. Mangin. Joint Bayesian Cortical Sulci Recognition and Spatial Normalization. In Proc. 21th IPMI, LNCS-5636, Williamsburg, VA, pages 176-187, July 2009..

Parameters

data_graph: Cortical folds graph ( input )

output_graph: Labelled Cortical folds graph ( output )

model: Sulci Segments Model ( input )

posterior_probabilities: Sulci Labels Segmentwise Posterior Probabilities ( output )

labels_translation_map: Label translation ( input )

labels_priors: Sulci Labels Priors ( input )

segments_relations_model: Sulci Segments Relations Model ( optional, input )

initial_transformation: Transformation matrix ( optional, input )

global_transformation: Sulci Talairach to Global SPAM transformation ( optional, input )

Technical information

Toolbox : Morphologist

User level : 2

Identifier : spam_recognitionmarkov

File name : brainvisa/toolboxes/morphologist/processes/Sulci/Recognition/spam_recognitionmarkov.py

Supported file formats :

data_graph :

Graph and data

output_graph :

Graph and data

model :

Text Data Table

posterior_probabilities :

CSV file

labels_translation_map :

Label Translation, DEF Label Translation

labels_priors :

Text Data Table

segments_relations_model :

Text Data Table

initial_transformation :

Transformation matrix

global_transformation :

Transformation matrix