Reliable Lung Segmentation Methodology by Including Juxtapleural Nodules

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Novo, J (Author) Other; Rouco, J (Author) FEUP; Mendonca, A (Author) FEUP; Aurelio Campilho (Author) FEUP

Conference proceedings International

Title: IMAGE ANALYSIS AND RECOGNITION, ICIAR 2014, PT II

Pages: 227-235

11th International Conference on Image Analysis and Recognition (ICIAR)

PORTUGAL, OCT 22-24, 2014

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 9 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00A-1VQ

DOI: 10.1007/978-3-319-11755-3_26

Abstract (EN): In a lung nodule detection task, parenchyma segmentation is crucial to obtain the region of interest containing all the nodules. Thus, the challenge is to devise a methodology that includes all the lung nodules, particularly those close to the walls, as the juxtapleural nodules. In this paper, different region growing approaches are proposed for the automatic segmentation of the lung parenchyma. The methodology is organized in five different steps: first, the image intensity is corrected to improve the contrast of the lungs. With that, the fat area is obtained, automatically deriving the interior of the lung region. Then, the traquea is extracted by a 3D region growing, being subtracted from the lung region results. The next step is the division of the two lungs to guarantee that both are separated. And finally, the lung contours are refined to provide appropriate final results. The methodology was tested in 50 images taken from the LIDC image database, with a large variability and, specially, including different types of lung nodules. In particular, this dataset contains 158 nodules, from which 40 are juxtapleural nodules. Experimental results demonstrate that the method provides accurate lung regions, specially including the centers of 36 of the juxtapleural nodules. For the other 4, although the centers are not included, parts of their areas are retained in the segmentation, which is useful for lung nodule detection.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9