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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Published in:	Medical physics (Lancaster) Vol. 30; no. 7; pp. 1592 - 1601
Main Authors:	Näppi, Janne, Yoshida, Hiroyuki
Format:	Journal Article
Language:	English
Published:	United States American Association of Physicists in Medicine 01.07.2003
Subjects:	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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Abstract	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.
AbstractList	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. 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.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.
Author	Yoshida, Hiroyuki Näppi, Janne
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Snippet	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... 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...
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SubjectTerms	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
Title	Feature-guided analysis for reduction of false positives in CAD of polyps for computed tomographic colonography
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