Conference Publication Details
Mandatory Fields
Duggan, N,Bae, E,Shen, SW,Hsu, W,Bui, A,Jones, E,Glavin, M,Vese, L,Tai, XC,Bae, E,Chan, TF,Lysaker, M
ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, EMMCVPR 2015
A Technique for Lung Nodule Candidate Detection in CT Using Global Minimization Methods
2015
January
Published
1
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Optional Fields
COMPUTER-AIDED DETECTION SELECTIVE ENHANCEMENT FILTERS IMAGE DATABASE CONSORTIUM PULMONARY NODULES DETECTION SYSTEM RADIOLOGISTS DETECTION DETECTION PERFORMANCE CHEST CT SCANS DIAGNOSIS
478
491
The first stage in computer aided pulmonary nodule detection schemes is a candidate detection step designed to provide a simplified representation of the lung anatomy, such that features like the lung wall, and large airways are removed leaving only data which has greater potential to be a nodule. Nodules which are connected to blood vessels tend to be characterized by irregular geometrical features which can result in their remaining undetected by rule-based classifiers relying only local image metrics. In the current paper a novel approach for lung nodule candidate detection is proposed based on the application of global segmentation methods combined with mean curvature minimization and simple rule-based filtering. Experimental results indicate that the proposed method can accurately detect nodules displaying a diverse range of geometrical features.
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