Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6855630 | Expert Systems with Applications | 2016 | 15 Pages |
Abstract
Different cases with pulmonary nodules from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were taken and used to analyze and validate the approaches. The chest CT images present a large variability in nodule characteristics and image conditions. Our proposals provide an accurate lung nodule segmentation, similar to radiologists performance. Our Hessian-based approaches were validated with 569 solid and mostly solid nodules demonstrating that these novel strategies have good results when compared with the radiologists segmentations, providing accurate pulmonary nodule volumes for posterior characterization and appropriate diagnosis.
Keywords
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
L. Gonçalves, J. Novo, A. Campilho,