Open AccessSpatial codification of label predictions in multi‐scale stacked sequential learning: a case study on multi‐class medical volume segmentation
Author(s) -
Sampedro Frederic,
Escalera Sergio
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0067
Subject(s) - computer science , segmentation , artificial intelligence , pattern recognition (psychology) , image segmentation , classifier (uml) , adaboost , independent and identically distributed random variables , machine learning , class (philosophy) , volume (thermodynamics) , data mining , mathematics , random variable , statistics , physics , quantum mechanics
In this study, the authors propose the spatial codification of label predictions within the multi‐scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non‐independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches.