Literature DB >> 1601603

Computerized scheme for the detection of pulmonary nodules. A nonlinear filtering technique.

H Yoshimura1, M L Giger, K Doi, H MacMahon, S M Montner.   

Abstract

To aid radiologists in the detection of lung cancer, the authors are developing a computer-aided diagnosis system that locates areas suspicious for nodules in digital chest radiographs. The system involves a difference-image approach and various feature-extraction techniques. The authors describe nonlinear filters used in the difference-image approach. A morphological open operation and a ring-shaped median filter are applied in the difference-image step for signal enhancement and signal suppression, respectively. Using 60 clinical chest radiographs, the nonlinear filtering method detected approximately 63% of actual nodules with approximately 19 false-positive results per image. The locations of the false-positive detections, however, usually did not coincide with those from the linear filtering method. Thus, by using a combination of the detections from the two methods, the false-positive rate was reduced to two to three per image at a sensitivity of 60%.

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Mesh:

Year:  1992        PMID: 1601603     DOI: 10.1097/00004424-199202000-00005

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  6 in total

1.  Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility.

Authors:  S G Armato; M L Giger; H MacMahon
Journal:  J Digit Imaging       Date:  1999-02       Impact factor: 4.056

2.  Detection of lung nodules in digital chest radiographs using artificial neural networks: a pilot study.

Authors:  Y C Wu; K Doi; M L Giger
Journal:  J Digit Imaging       Date:  1995-05       Impact factor: 4.056

Review 3.  Potential usefulness of digital imaging in clinical diagnostic radiology: computer-aided diagnosis.

Authors:  K Doi; M L Giger; R M Nishikawa; K R Hoffmann; H MacMahon; R A Schmidt
Journal:  J Digit Imaging       Date:  1995-02       Impact factor: 4.056

4.  Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme.

Authors:  Y C Wu; K Doi; M L Giger; C E Metz; W Zhang
Journal:  J Digit Imaging       Date:  1994-11       Impact factor: 4.056

5.  Quantitative analysis of geometric-pattern features of interstitial infiltrates in digital chest radiographs: preliminary results.

Authors:  S Katsuragawa; K Doi; H MacMahon; L Monnier-Cholley; J Morishita; T Ishida
Journal:  J Digit Imaging       Date:  1996-08       Impact factor: 4.056

6.  Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images.

Authors:  Michael Horry; Subrata Chakraborty; Biswajeet Pradhan; Manoranjan Paul; Douglas Gomes; Anwaar Ul-Haq; Abdullah Alamri
Journal:  Sensors (Basel)       Date:  2021-10-07       Impact factor: 3.576

  6 in total

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