A robust and efficient framework for tubular structure segmentation in chest CT images.

Background: Tubular structure segmentation in chest CT images can reduce false positives (FPs) dramatically and improve the performance of nodules malignancy levels classification.

Objective: In this study, we present a framework that can segment the pulmonary tubular structure regions robustly and efficiently.

Methods: Firstly, we formulate a global tubular structure identification model based on Frangi filter.

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions.

With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed wearable sensors provide a platform for comprehensively monitoring the status of soldiers, wearable technology based on multi-source fusion lacks a generalized research system to highlight the advantages of heterogeneous sensor networks and information fusion.

False positive reduction in pulmonary nodule classification using 3D texture and edge feature in CT images.

Background: Pulmonary nodule detection can significantly influence the early diagnosis of lung cancer while is confused by false positives.

Objective: In this study, we focus on the false positive reduction and present a method for accurate and rapid detection of pulmonary nodule from suspective regions with 3D texture and edge feature.

Methods: This work mainly consists of four modules.

A novel lung cancer detection algorithm for CADs based on SSP and Level Set.

The fuzzy degree of lung nodule boundary is the most important cue to judge the lung cancer in CT images. Based on this feature, the paper proposes a novel lung cancer detection method for CT images based on the super-pixels and the level set segmentation methods. In the proposed methods, the super-pixels method is used to segment the lung region and the suspected lung cancer lesion region in the CT image.