Authors:
Samya Amiri
1
;
Mohamed Ali Mahjoub
2
and
Islem Rekik
3
Affiliations:
1
University of Sousse, ENISo – National Engineering School of Sousse and University of Sousse, Tunisia
;
2
ENISo – National Engineering School of Sousse and University of Sousse, Tunisia
;
3
School of Science and Engineering, Computing and University of Dundee, United Kingdom
Keyword(s):
Ensemble Classifiers, Dynamic Learning, Autocontext Model, Structured Random Forest, Bayesian Network, Brain Tumor Segmentation, MRI.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
We introduce a dynamic multiscale tree (DMT) architecture that learns how to leverage the strengths of different
state-of-the-art classifiers for supervised multi-label image segmentation. Unlike previous works that
simply aggregate or cascade classifiers for addressing image segmentation and labeling tasks, we propose to
embed strong classifiers into a tree structure that allows bi-directional flow of information between its classifier
nodes to gradually improve their performances. Our DMT is a generic classification model that inherently
embeds different cascades of classifiers while enhancing learning transfer between them to boost up their classification
accuracies. Specifically, each node in our DMT can nest a Structured Random Forest (SRF) classifier
or a Bayesian Network (BN) classifier. The proposed SRF-BN DMT architecture has several appealing properties.
First, while SRF operates at a patch-level (regular image region), BN operates at the super-pixel level
(irregul
ar image region), thereby enabling the DMT to integrate multi-level image knowledge in the learning
process. Second, the proposed DMT robustly overcomes the limitations of the aggregated classifiers through
the ascending and descending flow of contextual information between each parent node and its children nodes.
Third, we train DMT using different scales to capture a coarse-to-fine image details. Last, DMT demonstrates
its outperformance in comparison to several state-of-the-art segmentation methods for multi-labeling of brain
images with gliomas.
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