Literature DB >> 3386584

Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields.

M L Giger1, K Doi, H MacMahon.   

Abstract

We are investigating the characteristic features of lung nodules and the surrounding normal anatomic background in order to develop an algorithm of computer vision for use as an aid in the detection of nodules in digital chest radiographs. Our technique involves an attempt to eliminate the background anatomic structures in the lung fields by means of a difference image approach. Then, feature-extraction techniques, such as tests for circularity, size, and their variation with threshold level, are applied so that suspected nodules can be isolated. Preliminary results of this automated detection scheme yielded high true-positive rates and low false-positive rates in the peripheral lung regions of the chest. This detection scheme, which can assist the final diagnosis by the clinician, has the potential to improve the early detection of lung carcinomas.

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Year:  1988        PMID: 3386584     DOI: 10.1118/1.596247

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  35 in total

1.  Automated lung segmentation of diseased and artifact-corrupted magnetic resonance sections.

Authors:  William F Sensakovic; Samuel G Armato; Adam Starkey; Philip Caligiuri
Journal:  Med Phys       Date:  2006-09       Impact factor: 4.071

Review 2.  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

Review 3.  Computer-aided diagnosis in medical imaging: historical review, current status and future potential.

Authors:  Kunio Doi
Journal:  Comput Med Imaging Graph       Date:  2007-03-08       Impact factor: 4.790

4.  Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier.

Authors:  Qiang Li; Feng Li; Kunio Doi
Journal:  Acad Radiol       Date:  2008-02       Impact factor: 3.173

Review 5.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

Authors:  Maryellen L Giger; Heang-Ping Chan; John Boone
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

6.  Sensitivity and specificity of a CAD solution for lung nodule detection on chest radiograph with CTA correlation.

Authors:  William Moore; Jennifer Ripton-Snyder; George Wu; Craig Hendler
Journal:  J Digit Imaging       Date:  2011-06       Impact factor: 4.056

Review 7.  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

8.  A review of computer-aided diagnosis in thoracic and colonic imaging.

Authors:  Kenji Suzuki
Journal:  Quant Imaging Med Surg       Date:  2012-09

9.  Computer-aided diagnosis for the classification of focal liver lesions by use of contrast-enhanced ultrasonography.

Authors:  Junji Shiraishi; Katsutoshi Sugimoto; Fuminori Moriyasu; Naohisa Kamiyama; Kunio Doi
Journal:  Med Phys       Date:  2008-05       Impact factor: 4.071

10.  Expert knowledge-infused deep learning for automatic lung nodule detection.

Authors:  Jiaxing Tan; Yumei Huo; Zhengrong Liang; Lihong Li
Journal:  J Xray Sci Technol       Date:  2019       Impact factor: 1.535

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