11 March 2014 Study of quality perception in medical images based on comparison of contrast enhancement techniques in mammographic images
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Abstract
With the absolute prevalence of digital images in mammography several new tools became available for radiologist; such as CAD schemes, digital zoom and contrast alteration. This work focuses in contrast variation and how the radiologist reacts to these changes when asked to evaluated image quality. Three contrast enhancing techniques were used in this study: conventional equalization, CCB Correction [1] – a digitization correction – and value subtraction. A set of 100 images was used in tests from some available online mammographic databases. The tests consisted of the presentation of all four versions of an image (original plus the three contrast enhanced images) to the specialist, requested to rank each one from the best up to worst quality for diagnosis. Analysis of results has demonstrated that CCB Correction [1] produced better images in almost all cases. Equalization, which mathematically produces a better contrast, was considered the worst for mammography image quality enhancement in the majority of cases (69.7%). The value subtraction procedure produced images considered better than the original in 84% of cases. Tests indicate that, for the radiologist’s perception, it seems more important to guaranty full visualization of nuances than a high contrast image. Another result observed is that the “ideal” scanner curve does not yield the best result for a mammographic image. The important contrast range is the middle of the histogram, where nodules and masses need to be seen and clearly distinguished.
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B. Matheus, L. B. Verçosa, B. Barufaldi, H. Schiabel, "Study of quality perception in medical images based on comparison of contrast enhancement techniques in mammographic images", Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90371I (11 March 2014); doi: 10.1117/12.2043430; https://doi.org/10.1117/12.2043430
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