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Erschienen in:
On the Expressivity of Alignment-Based Distance and Similarity Measures on Sequences and Trees in Inducing Orderings Kapitel lesen Erstes Kapitel lesen

2013 | OriginalPaper | Buchkapitel

A Robust Deformable Model for 3D Segmentation of the Left Ventricle from Ultrasound Data

verfasst von : Carlos Santiago, Jorge S. Marques, Jacinto C. Nascimento

Erschienen in: Mathematical Methodologies in Pattern Recognition and Machine Learning

Verlag: Springer New York

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Abstract

This paper presents a novel bottom-up deformable-based model for the segmentation of the Left Ventricle (LV) in 3D ultrasound data. The methodology presented here is based on Probabilistic Data Association Filter (PDAF). The main steps that characterize the proposed approach can be summarized as follows. After a rough initialization given by the user, the following steps are performed: (1) low-level transition edge points are detected based on a prior model for the intensity of the LV, (2) middle-level features or patch formation is accomplished by linking the low-level information, (3) data interpretations are computed (hypothesis) based on the reliability (belonging or not to the LV boundary) of the previously obtained patches, (4) a confidence degree is assigned to each data interpretation and the model is updated taking into account all data interpretations.Results testify the usefulness of the approach in both synthetic and real LV volumes data. The obtained LV segmentations are compared with expert’s manual segmentations, yielding an average distance of 3 mm between them.

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Metadaten
Titel
A Robust Deformable Model for 3D Segmentation of the Left Ventricle from Ultrasound Data
verfasst von
Carlos Santiago
Jorge S. Marques
Jacinto C. Nascimento
Copyright-Jahr
2013
Verlag
Springer New York
Buch
Mathematical Methodologies in Pattern Recognition and Machine Learning

Print ISBN: 978-1-4614-5075-7

Electronic ISBN: 978-1-4614-5076-4

Copyright-Jahr: 2013

DOI
https://doi.org/10.1007/978-1-4614-5076-4_11

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