Image Segmentation for Lung Lesions Using Ant Colony Optimization Classifier in Chest CT View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

Chii-Jen Chen

ABSTRACT

The chest computed tomography (CT) is the most commonly used imaging technique for the inspection of lung lesions. In order to provide the physician more valuable preoperative opinions, a powerful computer-aided diagnostic (CAD) system is indispensable. In this paper, we aim to develop an ant colony optimization (ACO-based) classifier to extract the lung mass. We could calculate some information such as its boundary, precise size, localization of tumors, and spatial relations. Final, we reconstructed the extracted lung and tumor regions to a 3D volume module to provide physicians the more reliable vision. In order to validate the proposed system, we have tested our method in a database from 15 lung patients. We also demonstrated the accuracy of the segmentation method using some power statistical protocols. The experiments indicate our method results more satisfied performance in most cases, and can help investigators detect lung lesion for further examination. More... »

PAGES

283-289

References to SciGraph publications

Book

TITLE

Advances in Intelligent Information Hiding and Multimedia Signal Processing

ISBN

978-3-319-63855-3
978-3-319-63856-0

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-63856-0_35

DOI

http://dx.doi.org/10.1007/978-3-319-63856-0_35

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090744385


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