A new fast morphological geodesic active contour method for lung CT image segmentation
| Autor: | Pedro Pedrosa Rebouças Filho, Lucas de O. Santos, Matheus T. Guimarães, Antônio Carlos da Silva Barros, Victor Hugo C. de Albuquerque, Solon Alves Peixoto, Elizângela de S. Rebouças, Aldisio G. Medeiros |
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| Rok vydání: | 2019 |
| Předmět: |
Active contour model
Jaccard index Correlation coefficient Computer science business.industry Applied Mathematics 020208 electrical & electronic engineering 010401 analytical chemistry Pattern recognition 02 engineering and technology Gold standard (test) Image segmentation Condensed Matter Physics 01 natural sciences 0104 chemical sciences Similarity (network science) Sørensen–Dice coefficient 0202 electrical engineering electronic engineering information engineering Segmentation Artificial intelligence Electrical and Electronic Engineering business Instrumentation |
| Zdroj: | Measurement. 148:106687 |
| ISSN: | 0263-2241 |
| DOI: | 10.1016/j.measurement.2019.05.078 |
| Popis: | This work proposes a new adaptive approach to lung segmentation based on a non-parametric adaptive active contour method (ACM) without previous training using the new Fuzzy Border Detector, called Fast Morphological Geodesic Active Contour (FGAC). Performance was evaluated with 72 lung images of volunteers that were with fibrosis, chronic obstructive pulmonary disease or were healthy. A manual segmentation by a medical specialist was considered the gold standard. The mean time of segmentation of seventy-two the two lungs was 1.98 s. This was the best average time among all the other segmentation methods compared here: GVF (240 s), VFC(30 s), OPS Euclidean (5.86 s), SISDEP (4.90 s), and CRAD (2 s); thus showing its potential for real-time applications. The FGAC showed good results in all similarity metrics compared in this work like the Jaccard Index (92.73%), Dice coefficient (96.19%) and Matthew correlation coefficient (95.54%), and also achieved good results in sensitivity (99.21%), and accuracy (98.86%). The new approach showed quantitative indexes better than the traditional methods VFC, GVF, RHT mod, RHT multi. Moreover, we evaluated the proposed method against the supervised techniques OPS-Euclidean, SISDEP, CRISP and CRAD. Our approach achieved superior or equivalent results to these methods, however with a shorter convergence time. These results had good indexes suggesting that the proposed approach can be used to aid medical diagnosis in pulmonology. |
| Databáze: | OpenAIRE |
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