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Lung Field Segmenting in Dual-Energy Subtraction Chest X-ray Images

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Abstract

The purpose of this study was to develop and test a method to delineate lung field boundaries in dual-energy chest x-ray images. The segmenting method uses soft-tissue images and spatial frequency–dependent, background-subtracted images. Large-scale chest anatomy features are located and used to select the lung apices, the lateral lung boundaries, and the lung–mediastinum and lung–diaphragm boundaries. Extraneous parts of the contours are removed and they are joined to form complete lung boundaries. The reliability measure uses a statistical shape model to estimate the probability of occurrence of a contour. The method was experimentally tested with 30 human subject images. It has higher accuracy and specificity and a sensitivity parameter equal to the best previously reported method. The reliability measure is able to detect contours with unusual lung outlines or errors in the processing. The method exploits the characteristics of dual-energy subtraction images to improve lung field segmenting performance.

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Acknowledgments

I acknowledge the assistance of Dr. Elizabeth H. Moore, Dr. J. Anthony Seibert of the University of California, Davis Medical Center, and Dr. Reginald Munden and Mr. Stephen K. Thompson of the University of Texas, M.D. Anderson Cancer Center in providing the human subject image data. I also acknowledge the assistance of Dr. Linda S. Maltz on statistical questions. This work was supported in part by National Institutes of Health SBIR grant R3CA97826A. Its contents are solely the responsibility of the author and do not necessarily represent the official views of the National Institutes of Health.

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  1. Aprend Technology, 2369 Laura Lane, Mountain View, California 94043

    Robert E. Alvarez Ph.D.

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  1. Robert E. Alvarez Ph.D.
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Alvarez, R.E. Lung Field Segmenting in Dual-Energy Subtraction Chest X-ray Images. J Digit Imaging 17, 45–56 (2004). https://doi.org/10.1007/s10278-003-1701-8

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