Feature-guided analysis for reduction of false positives in CAD of polyps for computed tomographic colonography

We evaluated the effect of our novel technique of feature-guided analysis of polyps on the reduction of false-positive (FP) findings generated by our computer-aided diagnosis (CAD) scheme for the detection of polyps from computed tomography colonographic data sets. The detection performance obtained...

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Vydané v:Medical physics (Lancaster) Ročník 30; číslo 7; s. 1592 - 1601
Hlavní autori: Näppi, Janne, Yoshida, Hiroyuki
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States American Association of Physicists in Medicine 01.07.2003
Predmet:
3D algorithms
Algorithms
cancer
Cluster Analysis
Colonic Polyps - classification
Colonic Polyps - diagnostic imaging
Colonography, Computed Tomographic - methods
Computed radiography
Computed tomography
computer aided detection
Computer aided diagnosis
computerised tomography
CT colonography
Data analysis
Diagnosis, Computer-Assisted - methods
Diseases
False Positive Reactions
feature extraction
Fuzzy Logic
Humans
Image analysis
Imaging, Three-Dimensional - methods
medical image analysis
medical image processing
pattern recognition
Pattern Recognition, Automated
Physicists
Quality Control
Reproducibility of Results
Sensitivity and Specificity
tumours
3D algorithms
computer aided detection
CT colonography
medical image analysis
pattern recognition
ISSN:0094-2405, 2473-4209
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Shrnutí:We evaluated the effect of our novel technique of feature-guided analysis of polyps on the reduction of false-positive (FP) findings generated by our computer-aided diagnosis (CAD) scheme for the detection of polyps from computed tomography colonographic data sets. The detection performance obtained by use of feature-guided analysis in the segmentation and feature analysis of polyp candidates was compared with that obtained by use of our previously employed fuzzy clustering technique. We also evaluated the effect of a feature called modified gradient concentration (MGC) on the detection performance. A total of 144 data sets, representing prone and supine views of 72 patients that included 14 patients with 21 colorectal polyps 5–25 mm in diameter, were used in the evaluation. At a 100% by-patient (95% by-polyp) detection sensitivity, the FP rate of our CAD scheme with feature-guided analysis based on round-robin evaluation was 1.3 (1.5) FP detections per patient. This corresponds to a 70–75 % reduction in the number of FPs obtained by use of fuzzy clustering at the same sensitivity levels. Application of the MGC feature instead of our previously used gradient concentration feature did not improve the detection result. The results indicate that feature-guided analysis is useful for achieving high sensitivity and a low FP rate in our CAD scheme.
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ISSN:0094-2405
2473-4209
DOI:10.1118/1.1576393