Learning human observer models for image
quality evaluation
J. Brankov , M. Wernick, Y. Yang,
L. Wei
It is now widely accepted that the most appropriate way to judge image quality
is to measure the ability of an image to serve its intended purpose. Toward
this end, the channelized Hotelling observer (CHO) has been widely used to
model human observer performance in medical lesion-detection tasks. We have
developed an alternative approach, based on machine learning, in which we specifically
aim to learn the human-observer model from actual data reported by humans in
response to images presented to them. Specifically, we have found that support
vector machines can significantly outperform the CHO in correctly modeling
human observer performance. This work is sponsored by NIH/NHLBI.
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There
is considerable current interest in finding methods to improve
the diagnostic accuracy of mammographic interpretation. We
are studying one approach, in which the radiologist is presented
with mammograms from past cases that the computer concludes
are relevant to the case being evaluated, in effect providing
an online atlas of images with known pathology. To make such
an approach useful, it is important to define “relevance” of
the images in an effective way. We were the first to propose
the use of machine learning techniques to learn the concept
of relevance directly from radiologists, in the form of similarity
scores. We have shown that such an approach can yield to
image retrieval results that closely match the preferences
of the user. 
the ability to automatically detect clustered microcalcifications in digital
mammograms. We have developed two modern machine-learning methods for calc detection—based
on the relevance vector machine (RVM) and support vector machine (SVM)—which
significantly outperform other techniques. This demonstrates that there is still
a great deal of performance to be gained in CAD by introducing modern tools from
statistical learning theory.