Improving polyp detection algorithms for CT colonography: Pareto front approach

NCUIR > Research Center > Research Center for adaptive Data analysis > journal & Dissertation > Item 987654321/50649

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/50649

Title:	Improving polyp detection algorithms for CT colonography: Pareto front approach
Authors:	Huang,A;Li,JA;Summers,RM;Petrick,N;Hara,AK
Contributors:	數據分析方法研究中心
Keywords:	COMPUTER-AIDED DIAGNOSIS;TOMOGRAPHIC VIRTUAL COLONOSCOPY;SUPPORT VECTOR MACHINES;FINITE-SAMPLE SIZE;COLONIC POLYPS;COLORECTAL NEOPLASIA;FEATURE-SELECTION;NEURAL-NETWORKS;CLASSIFICATION;SEGMENTATION
Date:	2010
Issue Date:	2012-03-27 17:50:13 (UTC+8)
Publisher:	國立中央大學
Abstract:	We investigated a Pareto front approach to improve polyp detection algorithms for CT colonography (CTC). A dataset of 56 CTC colon surfaces with 87 proven positive detections of 53 polyps sized 4-60 mm was used to evaluate the performance of a one-step and a two-step curvature-based region growing algorithm. The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms. The false positive rate was lower (p < 0.05) by the two-step algorithm than by the one-step for 63% of all possible operating points. While operating at a suitable sensitivity level such as 90.8% (79/87) or 88.5% (77/87), the false positive rate was reduced by 24.4% (95% confidence intervals 17.9-31.0%) or 45.8% (95% confidence intervals 40.1-51.0%), respectively. We demonstrated that, with a proper experimental design, the Pareto optimization process can effectively help in fine-tuning and redesigning polyp detection algorithms. (C) 2010 Elsevier B.V. All rights reserved.
Relation:	PATTERN RECOGNITION LETTERS
Appears in Collections:	[Research Center for adaptive Data analysis ] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	686	View/Open

社群 sharing

Loading...