Abstract

Abstract
Lung nodule segmentation allows for automatic measurement of the nodule's size or volume which is of utmost importance in lung cancer diagnosis. It is a challenging task since there are many different types of nodules (solid or non-solid, solitary or multiple, etc). A supervised lung nodule segmentation method uses a shape-based, contrast-based and intensity-based feature set to produce three preliminary segmentations and an artificial neural network to obtain a more accurate segmentation. This method was applied to 20 computer tomography studies, all containing nodules. The data has 10 images of solid nodules and 10 images of ground glass opacity nodules, all with ground-truth. The segmentation uses a region growing approach and the volumetric shape index is used for nodule detection and providing a seed point. In the first and second segmentation the probability of each neighbor belonging to the nodule is estimated using the volumetric shape index and the convergence index filter, respectively. The third segmentation is obtained using a feature set region regression method where for each neighbor the probability of belonging to the nodule or not is obtained using k nearest neighbor regression. Then, using a leave-one out method, an artificial neural network uses the three preliminary segmentations as input and is trained to obtain a more accurate segmentation. Results obtained a 12% relative volume error, 88% and 93% Jaccard and Dice coefficient respectively. © 2014, Springer International Publishing Switzerland.

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.

Abstract

Abstract

Abstract
In this paper, a Hessian-based strategy, based on the central medialness adaptive principle, was adapted and proposed in a multiscale approach for the 3D segmentation of pulmonary nodules in chest CT scans. This proposal is compared with another well stated Hessian based strategy of the literature, for nodule extraction, in order to demonstrate its accuracy. Several scans from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were employed in the test and validation procedure. The scans include a large and heterogeneous set of 569 solid and mostly solid nodules with a large variability in the nodule characteristics and image conditions. The results demonstrated that the proposal offers correct results, similar to the performance of the radiologists, providing accurate nodule segmentations that perform the desirable scenario for a posterior analysis and the eventual lung cancer diagnosis.