Paper
17 April 1995 Detectability of radiological images: the influence of anatomical noise
Francois O. Bochud, Francis R. Verdun, Christian Hessler, Jean-Francois Valley
Author Affiliations +
Abstract
Radiological image quality can be objectively quantified by the statistical decision theory. This theory is commonly applied with the noise of the imaging system alone (quantum, screen and film noises) whereas the actual noise present on the image is the 'anatomical noise' (sum of the system noise and the anatomical texture). This anatomical texture should play a role in the detection task. This paper compares these two kinds of noises by performing 2AFC experiments and computing the area under the ROC-curve. It is shown that the 'anatomical noise' cannot be considered as a noise in the sense of Wiener spectrum approach and that the detectability performance is the same as the one obtained with the system noise alone in the case of a small object to be detected. Furthermore, the statistical decision theory and the non- prewhitening observer does not match the experimental results. This is especially the case in the low contrast values for which the theory predicts an increase of the detectability as soon as the contrast is different from zero whereas the experimental result demonstrates an offset of the contrast value below which the detectability is purely random. The theory therefore needs to be improved in order to take this result into account.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Francois O. Bochud, Francis R. Verdun, Christian Hessler, and Jean-Francois Valley "Detectability of radiological images: the influence of anatomical noise", Proc. SPIE 2436, Medical Imaging 1995: Image Perception, (17 April 1995); https://doi.org/10.1117/12.206845
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Cited by 19 scholarly publications.
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KEYWORDS
Signal to noise ratio

Imaging systems

Image quality

Fourier transforms

Modulation transfer functions

Optical spheres

Visualization

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