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Efficient Monte Carlo Image Analysis for the Location of Vascular Entity.

Authors :
Skibbe, Henrik
Reisert, Marco
Maeda, Shin-ichi
Koyama, Masanori
Oba, Shigeyuki
Ito, Kei
Ishii, Shin
Source :
IEEE Transactions on Medical Imaging; Feb2015, Vol. 34 Issue 2, p628-643, 16p
Publication Year :
2015

Abstract

Tubular shaped networks appear not only in medical images like X-ray-, time-of-flight MRI- or CT-angiograms but also in microscopic images of neuronal networks. We present EMILOVE (Efficient Monte-carlo Image-analysis for the Location Of Vascular Entity), a novel modeling algorithm for tubular networks in biomedical images. The model is constructed using tablet shaped particles and edges connecting them. The particles encode the intrinsic information of tubular structure, including position, scale and orientation. The edges connecting the particles determine the topology of the networks. For simulated data, EMILOVE was able to accurately extract the tubular network. EMILOVE showed high performance in real data as well; it successfully modeled vascular networks in real cerebral X-ray and time-of-flight MRI angiograms. We also show some promising, preliminary results on microscopic images of neurons. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
02780062
Volume :
34
Issue :
2
Database :
Complementary Index
Journal :
IEEE Transactions on Medical Imaging
Publication Type :
Academic Journal
Accession number :
100776865
Full Text :
https://doi.org/10.1109/TMI.2014.2364404