Solitary Pulmonary nodule segmentation based on pyramid and improved grab cut. (February 2021)

• Record Type: Journal Article
• Authors: Wang, Dan; He, Kun; Wang, Bin; Liu, Xiaoju; Zhou, Jiliu
• Is Part Of: Computer methods and programs in biomedicine. Volume 199(2021)
• Publication Date: 2021-02
• Subjects: Digital radiography images -- Solitary pulmonary nodule segmentation -- Total variance pyramid -- Multiscale appearance; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105910
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A solitary pulmonary nodule segmentation method for digital radiography is proposed, which combined the enhanced total-variance pyramid and improved Grab cut. The edge-preserving multi-resolution pyramid was constructed to provide the contour nearby curves and shorten the time of energy decreasing. The multiscale information was incorporated to optimize the edge term and improve the appearance model, it could overcome the inhomogeneity and fuzzy contours in solitary pulmonary nodule cases. Abstract: Background and objective: Accurate segmentation of solitary pulmonary nodule of digital radiography image is essential for lesion appearance measurement and medical follow-up. However, the imaging characteristics of digital radiography, the inhomogeneity and fuzzy contours of nodules often lead to poor performances. This work aims to develop a segmentation framework that satisfies the requirements of accurate segmentation. Methods: In this work, an interactive segmentation method which combined the enhanced total-variance pyramid and improved Grab cut was proposed to improve the performance of nodule segmentation. The edge-preserving multi-resolution pyramid structure did the rough segmentation on low resolution images, which provided contour nearby curves to initial the following accuracy segmentation and shortened the time of energy decreasing. With the multiscale information being incorporated to optimize the edge term and improve the appearance model, a novel Gibbs energy functional was constructed to extract the nodule in a proper scale. By introducing the multiscale processing and optimizing the energy terms, the proposed method could overcome the inhomogeneity and fuzzy contours. Results: For evaluation of the nodule segmentation, quantitative metrics such as precision, intersection over union, and dice similarity coefficient were introduced and compared in the experimental part. The proposed solitary pulmonary nodule segmentation method produced the results with mean values of precision 0.957, dice similarity coefficient 0.933, and intersection over union 0.891, respectively. And the corresponding standard deviation values were 0.041, 0.047, and 0.045. Conclusions: From the quantitative assessment and comparison in the experiments, the proposed method achieved a competitive performance in accuracy and stability, even in the cases with low contrast and fuzzy contours.