Improving image quality around subtle lung nodules by reducing artifacts in similar subtraction imaging
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Similar subtraction imaging is useful for the detection of lung nodules; however, some artifacts on similar subtraction images reduce their utility. The authors attempted to improve the image quality of similar subtraction images by reducing artifacts caused by differences in image contrast and sharpness between two images used for similar subtraction imaging. Image contrast was adjusted using the histogram specification technique. The differences in image sharpness were compensated for using a pixel matching technique. The improvement in image quality was evaluated objectively based on the degree of artifacts and the contrast-to-noise ratio (CNR) of the lung nodules. The artifacts in similar subtraction images were reduced in 94% (17/18) of cases, and CNR was improved in 83% (15/18) of cases. The results indicate that the combination of histogram specification and pixel matching techniques is potentially useful in improving image quality in similar subtraction imaging.
KeywordsChest radiograph Computer-aided diagnosis Similar subtraction imaging
The authors thank Keishin Kawamoto, M.Sc., R.T., Shun Tsubaki, M.Sc., R.T., Yusuke Kawazoe, B.Sc., R.T., and Yayoi Sakata, B.Sc, R.T. from the Morishita laboratory for their valuable contribution to discussion.
Compliance with ethical standards
Conflict of interest
All authors have no conflicts of interest to declare.
All procedures in studies involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Requirements for informed consent were waived for all images used in this study by the Institutional Review Board.
- 5.Oda A, Aoki T, Okazaki H, Kakeda S, Kourogi Y, Yahara K, Shouno H. Development of computerized system for selection of similar images from different patients for image subtraction of chest radiographs. Jpn Trans JR Med Biol Eng. 2006;44(3):435–444 (in Japanese).Google Scholar
- 13.Gonzalez RC. Image enhancement by histogram modification techniques. In: Wints P, editor. Digital image processing, 2nd ed. New York: Longman Higher Education; 1987. pp. 144–160.Google Scholar
- 14.Burger W, Burge MJ. Template matching in intensity images. In: Mackie L, editors. Principles of digital image processing: core algorithms. New York: Springer; 2009. pp. 257–66.Google Scholar