Model-based detection of lung nodules in computed tomography exams1

McCulloch, Colin; Kaucic, R.; Mendonça, Paulo R. S.; Walter, Deborah; Avila, Ricardo S.

doi:10.1016/s1076-6332(03)00729-3

Cited by 66 publications

(35 citation statements)

References 10 publications

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“…CAD schemes for lung nodule detection in thin-section CT images have been developed by some investigators [14][15][16][17][18][19][20][21][22]. A major disadvantage of some current CAD schemes is the use of a relatively small database.…”

Section: Discussionmentioning

confidence: 99%

“…We believe that comparison of our CAD scheme with these CAD schemes is inappropriate. In the remaining two studies, Paik et al [20] used a leave-one-out method to evaluate the performance levels of their CAD scheme based on a database of 8 CT scans with an unknown number of solid nodules, and they achieved a sensitivity of 80% with 1.3 false positives per scan or a sensitivity of 90% with 5.6 false positives per scan; McCulloch et al [19] used a cross-validation method to evaluate the performance levels of their CAD scheme based on a database of 50 CT scans with 43 nodules (35 solid and 8 GGO nodules), and they achieved a sensitivity of 70% with 8.3 false positives per scan. In this study, we employed a cross-validation method to evaluate the performance levels of our CAD scheme based on a database of 117 CT scans with 153 nodules (101 solid and 52 GGO nodules), and we achieved a sensitivity of 81% with 3.3 false positive per scan, or a sensitivity of 86% with 6.6 false positive per scan.…”

Section: Discussionmentioning

confidence: 99%

“…Since 2001, some investigators have reported their efforts in the development of CAD schemes for lung nodule detection in thin-section CT images [14][15][16][17][18][19][20][21][22]. Because the section thickness in thin-section CT is small, three-dimensional (3D) image processing and analysis techniques become applicable.…”

Section: Introductionmentioning

confidence: 99%

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Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

Li¹,

Li²,

Doi³

2008

Academic Radiology

133

105

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We have been developing a computer-aided diagnostic (CAD) scheme for lung nodule detection in order to assist radiologists in the detection of lung cancer in thin-section computed tomography (CT) images. Our database consisted of 117 thin-section CT scans with 153 nodules, obtained from a lung cancer screening program at a Japanese university (85 scans, 91 nodules) and from clinical work at an American university (32 scans, 62 nodules). The database included nodules of different sizes (4-28 mm, mean 10.2 mm), shapes, and patterns (solid and ground-glass opacity (GGO)). Our CAD scheme consisted of modules for lung segmentation, selective nodule enhancement, initial nodule detection, feature extraction, and classification. The selective nodule enhancement filter was a key technique for significant enhancement of nodules and suppression of normal anatomic structures such as blood vessels, which are the main sources of false positives. Use of an automated rule-based classifier for reduction of false positives was another key technique; it resulted in a minimized overtraining effect and an improved classification performance. We employed a case-based four-fold cross-validation testing method for evaluation of the performance levels of our computerized detection scheme. Our CAD scheme achieved an overall sensitivity of 86% (small: 76%, medium-sized: 94%, large: 95%; solid: 86%, mixed GGO: 89%, pure GGO: 81%) with 6.6 false positives per scan; an overall sensitivity of 81% (small: 69%, medium-sized: 91%, large: 91%; solid: 79%, mixed GGO: 88%, pure GGO: 81%) with 3.3 false positives per scan; and an overall sensitivity of 75% (small: 60%, medium-sized: 88%, large: 87%; solid: 70%, mixed GGO: 87%, pure GGO: 81%) with 1.6 false positives per scan. The experimental results indicate that our CAD scheme with its two key techniques can achieve a relatively high performance for nodules presenting large variations in size, shape, and pattern.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

Li¹,

Li²,

Doi³

2008

Academic Radiology

133

105

View full text Add to dashboard Cite

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“…Solid and subsolid pulmonary modules in CT images were automatically detected using model-based CAD system, which was described by McCulloch et al [14]. In this technique, multiple segmentation algorithms have been used to extract the noteworthy structures in the lungs.…”

Section: Introductionmentioning

confidence: 99%

Medical Decision-Making System of Ultrasound Carotid Artery Intima–Media Thickness Using Neural Networks

Santhiyakumari¹,

Rajendran²,

Madheswaran³

2010

J Digit Imaging

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The objective of this work is to develop and implement a medical decision-making system for an automated diagnosis and classification of ultrasound carotid artery images. The proposed method categorizes the subjects into normal, cerebrovascular, and cardiovascular diseases. Two contours are extracted for each and every preprocessed ultrasound carotid artery image. Two types of contour extraction techniques and multilayer back propagation network (MBPN) system have been developed for classifying carotid artery categories. The results obtained show that MBPN system provides higher classification efficiency, with minimum training and testing time. The outputs of decision support system are validated with medical expert to measure the actual efficiency. MBPN system with contour extraction algorithms and preprocessing scheme helps in developing medical decision-making system for ultrasound carotid artery images. It can be used as secondary observer in clinical decision making.

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“…These classifiers use the features to identify candidate objects either in the nodules set or in the non-nodule set. Several techniques can be used as classifiers in the final stage of nodule detection: based on either rules or linear classifiers (Lee et al, 2001), (Mekada et al, 2003), (Chang et al, 2004), by combining models (template matching) (Brown et al, 2003), analysis of the nearest cluster (Ezoe et al, 2002), (Tanino et al, 2003), support vector machine (Lu et al, 2004), (Mousa & Khan, 2002), , neural networks (Suzuki et al, 2008), (Lo et al, 2003), (Zhang et al, 2004) and Bayesian classifier (Farag et al, 2004), (McCulloch et al, 2004). The features mostly used for classification are those based on the density of voxels, description of shapes, spatial relation and size information.…”

mentioning

confidence: 99%

Informatics and Computerized Tomography Aiding Detection and Diagnosis of Solitary Lung Cancer

Silva¹,

Paiva²,

Nunes³

et al. 2011

Theory and Applications of CT Imaging and Analysis

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Model-based detection of lung nodules in computed tomography exams1

Cited by 66 publications

References 10 publications

Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

Medical Decision-Making System of Ultrasound Carotid Artery Intima–Media Thickness Using Neural Networks

Informatics and Computerized Tomography Aiding Detection and Diagnosis of Solitary Lung Cancer

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