Human observer detection experiments with mammograms and power‐law noise

Abstract

We determined contrast thresholds for lesion detection as a function of lesion size in both mammograms and filtered noise backgrounds with the same average power spectrum, P(f)=B/f3. Experiments were done using hybrid images with digital images of tumors added to digitized normal backgrounds, displayed on a monochrome monitor. Four tumors were extracted from digitized specimen radiographs. The lesion sizes were varied by digital rescaling to cover the range from 0.5 to 16 mm. Amplitudes were varied to determine the value required for 92% correct detection in two-alternative forced-choice (2AFC) and 90% for search experiments. Three observers participated, two physicists and a radiologist. The 2AFC mammographic results demonstrated a novel contrast-detail (CD) diagram with threshold amplitudes that increased steadily (with slope of 0.3) with increasing size for lesions larger than 1 mm. The slopes for prewhitening model observers were about 0.4. Human efficiency relative to these models was as high as 90%. The CD diagram slopes for the 2AFC experiments with filtered noise were 0.44 for humans and 0.5 for models. Human efficiency relative to the ideal observer was about 40%. The difference in efficiencies for the two types of backgrounds indicates that breast structure cannot be considered to be pure random noise for 2AFC experiments. Instead, 2AFC human detection with mammographic backgrounds is limited by a combination of noise and deterministic masking effects. The search experiments also gave thresholds that increased with lesion size. However, there was no difference in human results for mammographic and filtered noise backgrounds, suggesting that breast structure can be considered to be pure random noise for this task. Our conclusion is that, in spite of the fact that mammographic backgrounds have nonstationary statistics, models based on statistical decision theory can still be applied successfully to estimate human performance.

Digital mammography: what do we and what don’t we know?

“…By adding a structured background to the CDMAM phantom, Grosjean and Muller were able to show that while visibility of small details <0.4 mm was still limited by noise sources related to the image acquisition process, detection of larger low-contrast objects was mainly determined by the structured background [22]. This is in keeping with results from earlier experimental work based on digitized film [23,24] and matches the experience from clinical practice. While adequate detector dose and low quantum noise levels are necessary to adequately show microcalcifications, visibility of larger masses is much less affected by image noise associated with lower dose or inferior DQE of the detector (Fig.…”

Verification of DICOM GSDF in Complex Backgrounds

“…The variance also highlights the need to employ a large number of readers in studies of this nature, if power is to be at a reasonably acceptable level. In addition, the high variance calls into question the power achieved in the previous detection threshold work where Burgess [11,12] and Wang et al [15] used only three and six readers, respectively. These results may also raise the question as to whether the experience of the reader needs to be standardized, even with tasks that do not require medical, or specifically radiological, expertise.…”

“…These papers also used a two-alternative forced-choice signal known exactly (SKE) experimental methodology, removing search from the task even though radiologists use search when interpreting clinical images. Important papers on contrast such as those by Burgess et al [11,12] and Peli [13,14] either used an early form of Barten's work to perceptually linearize the monitors or used uncalibrated monitors. In these experiments, search was also eliminated.…”

Alternative Exposure Parameters and Post Process Noise Reduction Expect Considerable Dose Reduction in Single Mammography Views—Initial Experience on Mastectomy Specimens

“…A phantom, as used by Samei et al [1], does not have the ligaments and glandular structures of a breast, thereby lacking the most prominent source of noise, i.e. anatomical noise [10]. Processing real mammograms, as performed in the study by Chawla et al [2], has the drawback of noise level and pathology simulation, which might be an oversimplified reflection of image modifications seen at low doses.…”