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Title: A novel computer-aided lung nodule detection system for CT images

Abstract

Purpose: The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem. Methods: The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database. Results: The overall performancemore » of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter. Conclusions: A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.« less

Authors:
; ; ; ;  [1]
  1. Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel (Belgium)
Publication Date:
OSTI Identifier:
22100608
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 38; Journal Issue: 10; Other Information: (c) 2011 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; ALGORITHMS; BLOOD VESSELS; CAT SCANNING; CLASSIFICATION; COORDINATES; DETECTION; EXTRACTION; FLEXIBILITY; IMAGE PROCESSING; LUNGS; NEOPLASMS; NEURAL NETWORKS; PERFORMANCE; SENSITIVITY; TOPOLOGY

Citation Formats

Tan, Maxine, Deklerck, Rudi, Jansen, Bart, Bister, Michel, Cornelis, Jan, Department of Electronics and Informatics, and Department of Electronics and Informatics. A novel computer-aided lung nodule detection system for CT images. United States: N. p., 2011. Web. doi:10.1118/1.3633941.
Tan, Maxine, Deklerck, Rudi, Jansen, Bart, Bister, Michel, Cornelis, Jan, Department of Electronics and Informatics, & Department of Electronics and Informatics. A novel computer-aided lung nodule detection system for CT images. United States. doi:10.1118/1.3633941.
Tan, Maxine, Deklerck, Rudi, Jansen, Bart, Bister, Michel, Cornelis, Jan, Department of Electronics and Informatics, and Department of Electronics and Informatics. Sat . "A novel computer-aided lung nodule detection system for CT images". United States. doi:10.1118/1.3633941.
@article{osti_22100608,
title = {A novel computer-aided lung nodule detection system for CT images},
author = {Tan, Maxine and Deklerck, Rudi and Jansen, Bart and Bister, Michel and Cornelis, Jan and Department of Electronics and Informatics and Department of Electronics and Informatics},
abstractNote = {Purpose: The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem. Methods: The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database. Results: The overall performance of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter. Conclusions: A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.},
doi = {10.1118/1.3633941},
journal = {Medical Physics},
issn = {0094-2405},
number = 10,
volume = 38,
place = {United States},
year = {2011},
month = {10}
}