Literature DB >> 9719856

Computer-aided diagnosis for pulmonary nodules based on helical CT images.

K Kanazawa1, Y Kawata, N Niki, H Satoh, H Ohmatsu, R Kakinuma, M Kaneko, N Moriyama, K Eguchi.   

Abstract

In this paper, we present a computer-assisted automatic diagnostic system for lung cancer that detects nodule candidates at an early stage from helical CT images of the thorax. Our diagnostic system consists of analytical and diagnostic procedures. In the analytical procedure, first we extract the lung and the pulmonary blood vessel regions using the fuzzy clustering algorithm, then we analyze the features of these regions using image-processing techniques. In the diagnostic procedure, we define diagnostic rules utilizing the extracted features which support the determination of the candidate nodule locations. We show the effectiveness of our system by giving the results from its application to image data for mass screening of 450 patients.

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Year:  1998        PMID: 9719856     DOI: 10.1016/s0895-6111(98)00017-2

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  29 in total

1.  Estimation of expiratory time constants via fuzzy clustering.

Authors:  Marlies S Lourens; Lejla Ali; Bart van den Berg; Anton F M Verbraak; Jan M Bogaard; Henk C Hoogsteden; Robert Babuska
Journal:  J Clin Monit Comput       Date:  2002-01       Impact factor: 2.502

2.  Evaluation of a method of computer-aided detection (CAD) of pulmonary nodules with computed tomography.

Authors:  G Foti; N Faccioli; M D'Onofrio; A Contro; T Milazzo; R Pozzi Mucelli
Journal:  Radiol Med       Date:  2010-06-23       Impact factor: 3.469

3.  Insertion of virtual pulmonary nodules in CT data of the chest: development of a software tool.

Authors:  Hoen-oh Shin; Matthias Blietz; Bernd Frericks; Stefan Baus; Dagmar Savellano; Michael Galanski
Journal:  Eur Radiol       Date:  2006-07-04       Impact factor: 5.315

4.  Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.

Authors:  Ted W Way; Lubomir M Hadjiiski; Berkman Sahiner; Heang-Ping Chan; Philip N Cascade; Ella A Kazerooni; Naama Bogot; Chuan Zhou
Journal:  Med Phys       Date:  2006-07       Impact factor: 4.071

5.  Evaluation of the image quality of temporal subtraction images produced automatically in a PACS environment.

Authors:  Shuji Sakai; Hiroyasu Soeda; Akio Furuya; Hidetake Yabuuchi; Takashi Okafuji; Keiji Yamamoto; Hiroshi Honda; Kunio Doi
Journal:  J Digit Imaging       Date:  2006-12       Impact factor: 4.056

Review 6.  Recent progress in computer-aided diagnosis of lung nodules on thin-section CT.

Authors:  Qiang Li
Journal:  Comput Med Imaging Graph       Date:  2007-03-21       Impact factor: 4.790

7.  Adaptive border marching algorithm: automatic lung segmentation on chest CT images.

Authors:  Jiantao Pu; Justus Roos; Chin A Yi; Sandy Napel; Geoffrey D Rubin; David S Paik
Journal:  Comput Med Imaging Graph       Date:  2008-06-02       Impact factor: 4.790

Review 8.  Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review.

Authors:  Heang-Ping Chan; Lubomir Hadjiiski; Chuan Zhou; Berkman Sahiner
Journal:  Acad Radiol       Date:  2008-05       Impact factor: 3.173

9.  Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.

Authors:  Kenji Suzuki
Journal:  IEICE Trans Inf Syst       Date:  2013-04-01

10.  Computer-aided diagnosis systems for lung cancer: challenges and methodologies.

Authors:  Ayman El-Baz; Garth M Beache; Georgy Gimel'farb; Kenji Suzuki; Kazunori Okada; Ahmed Elnakib; Ahmed Soliman; Behnoush Abdollahi
Journal:  Int J Biomed Imaging       Date:  2013-01-29
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