Hessian based approaches for 3D lung nodule segmentation. (1st November 2016)

• Record Type: Journal Article
• Authors: Gonçalves, L.; Novo, J.; Campilho, A.
• Is Part Of: Expert systems with applications. Volume 61(2016)
• Page Start: 1
• Page End: 15
• Publication Date: 2016-11-01
• Subjects: Computer-aided diagnosis -- Thoracic CT imaging -- Lung cancer -- Pulmonary nodule segmentation -- Hessian-based approaches; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.05.024
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: New approaches for lung nodule segmentation are presented. Hessian based strategies were used to segment the nodule region. The central medialness adaptive approach offers more accurate results. The combination of both strategies joins their advantages. The combination of strategies outperforms individual strategy results. Abstract: In the design of computer-aided diagnosis systems for lung cancer diagnosis, an appropriate and accurate segmentation of the pulmonary nodules in computerized tomography (CT) is one of the most relevant and difficult tasks. An accurate segmentation is crucial for the posterior measurement of nodule characteristics and for lung cancer diagnosis. This paper proposes different approaches that use Hessian-based strategies for lung nodule segmentation in chest CT scans. We propose a multiscale segmentation process that uses the central medialness adaptive principle, a Hessian-based strategy that was originally formulated for tubular extraction but it also provides good segmentation results in blob-like structures as is the case of lung nodules. We compared this proposal with a well established Hessian-based strategy that calculates the Shape Index (SI) and Curvedness (CV). We adapted the SI and CV approach for multiscale nodule segmentation. Moreover, we propose the combination of both strategies by combining the results, in order to take benefit of the advantages of both strategies. 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.