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7 March 2022 A survey of image quality defects and their effect on the performance of an automated visual evaluation classifier
David Levitz, Sandeep Angara, Jose Jeronimo M.D., Ana Cecilia Rodriguez, Silvia de Sanjose, Sameer Antani, Mark W. Schiffman M.D.
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Proceedings Volume PC11950, Optics and Biophotonics in Low-Resource Settings VIII; PC1195003 (2022) https://doi.org/10.1117/12.2610213
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
Cervical cancer disproportionately affects low and middle income countries. Automated visual evaluation – using deep learning to analyze a digital cervix photograph – has been proposed for patient management. Image quality remains a key challenge, as it can be degraded by many types of image defects. A series of such defects were artificially added to a test set consisting of N=344 digitized cervigram images from existing studies. Replicate test sets were created for different image defects: blur, recoloring, obstructions of different colors and directions, rotations, and white Gaussian noise. The augmented images were evaluated by a classifier. The two most significant image defects were blur and Gaussian noise.
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David Levitz, Sandeep Angara, Jose Jeronimo M.D., Ana Cecilia Rodriguez, Silvia de Sanjose, Sameer Antani, and Mark W. Schiffman M.D. "A survey of image quality defects and their effect on the performance of an automated visual evaluation classifier", Proc. SPIE PC11950, Optics and Biophotonics in Low-Resource Settings VIII, PC1195003 (7 March 2022); https://doi.org/10.1117/12.2610213
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KEYWORDS
Image quality
Visualization
Digital photography
Cervix
Image analysis
Image quality standards
Photography
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