In this paper, we propose a novel classification method for the four types of lung nodules, ie,
well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed …

Detecting lung nodules with low-dose computed tomography (CT) can predict the future risk
suffering from lung cancers. However, there are a few studies on lung nodules with low-dose …

This paper examines the effectiveness of geometric feature descriptors, common in
computer vision, for false positive reduction and for classification of lung nodules in low dose …

This paper deals with segmentation of the lung tissues from low dose CT (LDCT) scans of
the chest. Goal is correct segmentation as well as maintaining the details of the lung region …

Lung nodules from low dose CT (LDCT) scans may be used for early detection of lung
cancer. However, these nodules vary in size, shape, texture, location, and may suffer from …

Abstract Model-based detection and classification of nodules are two major steps in CAD
systems design and evaluation. This paper examines feature-based nodule description for …

The quality of the lung nodule models determines the success of lung nodule detection. This
paper describes aspects of our data-driven approach for modeling lung nodules using the …

Lung nodule modeling quality defines the success of lung nodule detection. This paper
presents a novel method for generating lung nodules using variational level sets to obtain …

In this, paper a new approach for lung nodules detection from LDCT scans is proposed.
Intensity models of the nodules are generated using an active appearance model …

The success of lung nodule detection depends on the quality of nodule models. This paper
presents a novel approach for nodule modeling which is data-driven. Low dose CT (LDCT) …