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A Shift-Invariant Neural Network for the Lung Field Segmentation in Chest Radiography

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Abstract

We have developed a computerized method using a neural network for the segmentation of lung fields in chest radiography. The lung is the primary region of interest in routine chest radiography diagnosis. Since computer is expected to perform disease pattern search automatically, it is important to design appropriate algorithms to delineate the region of interest. A reliable segmentation method is essential to facilitate subsequent searches for image patterns associated with lung diseases. In this study, we employed a shift invariant neural network coupled with error back-propagation training method to extract the lung fields. A set of computer algorithms were also developed for smoothing the initially detected edges of lung fields. Our preliminary results indicated that 86% of the segmented lung fields globally matched the original chest radiographs. We also found that the method facilitates the development of computer algorithms in the field of computer-aided diagnosis.

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Authors and Affiliations

  1. ISIS Center, Dept. of Radiology, Georgetown University Medical Center, 2115 Wisconsin Ave., Suite 603, Washington, DC, 20007

    Akira Hasegawa, Shih-Chung B. Lo, Jyh-Shyan Lin, Matthew T. Freedman & Seong K. Mun

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  1. Akira Hasegawa
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  2. Shih-Chung B. Lo
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  3. Jyh-Shyan Lin
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  4. Matthew T. Freedman
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  5. Seong K. Mun
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Hasegawa, A., Lo, SC.B., Lin, JS. et al. A Shift-Invariant Neural Network for the Lung Field Segmentation in Chest Radiography. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 18, 241–250 (1998). https://doi.org/10.1023/A:1007937214367

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