Technical Program

Paper Detail

Paper:	FR-PM-PS3.7
Session:	Image Guided Diagnosis, Surgery and Therapy
Time:	Friday, April 7, 13:30 - 14:50
Presentation:	Poster
Title:	A Hybrid Approach for Automated Detection of Lung Nodules in CT Images
Authors:	Jamshid Dehmeshki; Medicsight PLC
	Xujiong Ye; Medicsight PLC
	Manlio Valdivieso Casique; Medicsight PLC
	Xinyu Lin; Medicsight PLC
Abstract:	This paper presents a novel shape based Genetic Algorithm Template Matching (GATM) method for the automated detection of lung nodules. The GA process is employed as an optimisation method to effectively search for the location of nodule candidates within the lung area. To define the fitness function for GATM, 3D geometric shape feature is calculated at each voxel and then combined into global nodule intensity distribution. Lung nodule phantom images are used as reference images for template matching. The proposed method has been validated on 70 clinical thoracic CT scans that contain 178 nodules as a gold standard. 151 nodules were detected by the proposed method, a detection rate of 85%, with the number of False Positives (FP) at approximately 14.0/scan. This high detection performance provides a good basis for a Computer-Aided Detection (CAD) system for lung nodules.

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