3D lung nodule candidate detection in multiple scales

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Novo, J (Author) Other; Goncalves, L (Author) Other; Ana Maria Mendonça (Author) FEUP; Aurélio Campilho (Author) FEUP

Conference proceedings International

Title: 2015 14th IAPR International Conference on Machine Vision Applications (MVA)

Pages: 61-64

14th IAPR International Conference on Machine Vision Applications (MVA)

Tokyo, JAPAN, MAY 18-22, 2015

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 11 Citations

Other information

Authenticus ID: P-00G-MPH

DOI: 10.1109/MVA.2015.7153133

Abstract (EN): Lung cancer is mainly diagnosed by the identification of malignant nodules in the lung parenchyma. For that purpose, the identification of all the possible structures that could be suspicious of lung nodules became a crucial task in any lung cancer computer aided diagnosis (CAD) system. In this paper, a new approach for lung nodule candidate identification is proposed. This method uses a 3D medialness Hessian-based filtering to identify round shape structures that could be identified as nodules. This technique, that demonstrated its accuracy in lung vesselness extraction, provides clearer candidates than other approaches, providing less response in the presence of noise artifacts and returns a better continuity in vessels, mostly responsible for false positives. That way, they will be better distinguishable from the nodules in posterior analysis. This approach was validated in 120 scans from the LIDC/IDRI image database. They include 212 nodules with diameters in the range 3 mm to 30 mm. The results demonstrate that our approach is capable of identifying most of the nodules and include less false positives than other approaches, facilitating a posterior task for false positive removal.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4