Dynamic Multiscale Tree Learning using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions

Samya Amiri, Mohamed Ali Mahjoub, Islem Rekik

2018

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 (irregular 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.

Paper Citation

in Harvard Style

Amiri S., Mahjoub M. and Rekik I. (2018). Dynamic Multiscale Tree Learning using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 419-426. DOI: 10.5220/0006630004190426

in Bibtex Style

@conference{visapp18,
author={Samya Amiri and Mohamed Ali Mahjoub and Islem Rekik},
title={Dynamic Multiscale Tree Learning using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={419-426},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006630004190426},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - Dynamic Multiscale Tree Learning using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions
SN - 978-989-758-290-5
AU - Amiri S.
AU - Mahjoub M.
AU - Rekik I.
PY - 2018
SP - 419
EP - 426
DO - 10.5220/0006630004190426
PB - SciTePress