Efficient automatic segmentation of tubular structures in images and volumes.
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Inclou dades d'ús des de 2022
Cita com:
hdl:2099.1/14214
Tipus de documentProjecte Final de Màster Oficial
Data2012-01
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
The segmentation of tubular structures is still an open eld of investigation,
particularly in medical imaging, where the quality of the image is poor with
respect to natural images. Despite the quality of state-of-the-art segmentation
methods, little effort has been devoted to the computational effi ciency
of the algorithms. E fficiency is an important topic, since intra-operative
computer assisted interventions require near real-time performance. In this
master thesis, we present a simple, yet effective, algorithm that e fficiently
segments vessels in 2D images and 3D volumes. The algorithm requires
no initialization and has a computational cost of O(SN logN), where S
is the number of scales and N is the number of image pixels. Results on
the DRIVE dataset show that the proposed method has near state-of-theart
performance with very little computational burden in the 2-dimensional
case. Qualitative results on the Rotterdam Coronary Artery dataset show
that the method is easily extendable to 3-dimensions.
Descripció
This work has been supported in part by the projects La Marató de TV3
082131, TIN2009-14404-C02, and CONSOLIDER-INGENIO CSD 2007-00018.
TitulacióMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2009)
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
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AdrianaRomeroSoriano.zip | Memòria | 4,386Mb | application/zip | Visualitza/Obre |