Automatic lung nodule detection using profile matching and back-propagation neural network techniques
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The potential advantages of using digital techniques instead of film-based radiography have been discussed extensively for the past 10 years. A major future application of digital techniques is computer-assisted diagnosis: the use of computer techniques to assist the radiologist in the diagnostic process. One aspect of this assistance is computer-assisted detection. The detection of small lung nodule has been recognized as a clinically difficult task for many years. Most of the literature has indicated that the rate for finding lung nodules (size range from 3 mm to 15 mm) is only approximately 65%, in those cases in which the undetected nodules could be found retrospectively. In recent published research, image processing techniques, such as thresholding and morphological analysis, have been used to enhance true-positive detection. However, these methods still produce many false-positive detections. We have been investigating the use of neural networks to distinguish true-positives nodule detections among those areas of interest that are generated from a signal enhanced image. The initial results show that the trained neural networks program can increase true-positive detections and moderately reduce the number of false-positive detections. The program reported here can perform three modes of lung nodule detection: thresholding, profile matching analysis, and neural network. This program is fully automatic and has been implemented in a DEC 5000/200 (Digital Equipment Corp, Maynard, MA) workstation. The total processing time for all three methods is less than 35 seconds. In this report, key image processing techniques and neural network for the lung nodule detection are described and the results of this initial study are reported.
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Boulehmi, H., Mahersia, H., & Hamrouni, K. (2025) · Springer eBooks · doi:10.1007/978-981-95-0401-5_14
Wavelets for Computer-Aided Diagnosis in Radiographic Images
Yoshida, H. (2001) · Computational imaging and vision · doi:10.1007/978-94-015-9715-9_14
Siddiqui, E. A. (2026) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2026.105748
- Authors
- Shih-Chung, B. L., Freedman, M. T., Lin, J.-S., & Mun, S. K.
- Affiliations
- Georgetown University Medical Center (US)
- Venue
- Journal of Digital Imaging
- Published
- 1993-02
- Volume
- 6
- Issue
- 1
- Pages
- 48-54
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- OpenAlex
- W1975154678
- Cited by
- 94
- References
- 17
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 1 · 2020: 3 · 2019: 4 · 2018: 3 · 2017: 2 · 2016: 2 · 2015: 1 · 2014: 2 · 2013: 3 · 2012: 2
- FWCI
- 7.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Imaging Informatics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Chicago
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- lung nodule detection, computer-aided detection, computer-aided diagnosis, profile matching, BP neural network, digital radiography, false positive reduction, thresholding, morphological analysis
- MeSH terms
- Algorithms, Solitary Pulmonary Nodule, Diagnosis, Computer-Assisted, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Predictive Value of Tests, Radiographic Image Enhancement, Neural Networks, Computer
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