Paper Info
Title
Automated matching of supine and prone colonic polyps based on PCA and SVMs
Abstract
Computed tomographic colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer screening. In current practice, a patient will be scanned twice during the CTC examination - once supine and once prone. In order to assist the radiologists in evaluating colon polyp candidates in both scans, we expect the computer aided detection (CAD) system can provide not only the locations of suspicious polyps, but also the possible matched pairs of polyps in two scans. In this paper, we propose a new automated matching method based on the extracted features of polyps by using principal component analysis (PCA) and Support Vector Machines (SVMs). Our dataset comes from the 104 CT scans of 52 patients with supine and prone positions collected from three medical centers. From it we constructed two groups of matched polyp candidates according to the size of true polyps: group A contains 12 true polyp pairs (> 9 mm) and 454 false pairs; group B contains 24 true polyp pairs (6-9 mm) and 514 false pairs. By using PCA, we reduced the dimensions of original data (with 157 attributes) to 30 dimensions. We did leave-one-patient-out test on the two groups of data. ROC analysis shows that it is easier to match bigger polyps than that of smaller polyps. On group A data, when false alarm probability is 0.20, the sensitivity of SVM achieves 0.83 which shows that automated matching of polyp candidates is practicable for clinical applications.
Year
DOI
Venue
2008
10.1117/12.769583
PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE)
Keywords
Field
DocType
computed tomographic colonography,computer aided detection,supine and prone polyp matching,principal component analysis,Support Vector Machines
False alarm,Pattern recognition,Support vector machine,Computer-aided diagnosis,Speech recognition,Colon polyps,Artificial intelligence,Virtual colonoscopy,Cancer screening,Supine position,Medicine,Principal component analysis
Conference
Volume
ISSN
Citations 
6915
0277-786X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Shijun Wang123922.83
R Van Uitert271.42
Ronald M. Summers317417.23