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International Journal of Biomedical Imaging - Volume 2016 2016, Article ID 7952541, 13 pages -

Research Article

Biomedical Engineering Department, Tel-Aviv University, 69978 Tel Aviv, Israel

Engineering Faculty, Bar-Ilan University, 52900 Ramat Gan, Israel

Received 29 October 2015; Revised 24 December 2015; Accepted 31 December 2015

Academic Editor: Yantian Zhang

Copyright © 2016 Roey Mechrez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents an automatic lesion segmentation method based on similarities between multichannel patches. A patch database is built using training images for which the label maps are known. For each patch in the testing image, similar patches are retrieved from the database. The matching labels for these patches are then combined to produce an initial segmentation map for the test case. Finally an iterative patch-based label refinement process based on the initial segmentation map is performed to ensure the spatial consistency of the detected lesions. The method was evaluated in experiments on multiple sclerosis MS lesion segmentation in magnetic resonance images MRI of the brain. An evaluation was done for each image in the MICCAI 2008 MS lesion segmentation challenge. Results are shown to compete with the state of the art in the challenge. We conclude that the proposed algorithm for segmentation of lesions provides a promising new approach for local segmentation and global detection in medical images.

Autor: Roey Mechrez, Jacob Goldberger, and Hayit Greenspan



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