The aim of the present work was to develop a method for simulating breast lesions in digital mammographic images.
Based on the visual appearance of real masses, three dimensional masses were created using a 3D random walk
method where the choice of parameters (number of walks and number of steps) enables one to control the
appearance of the simulated structure. This work is the first occasion that the random walk results have been
combined with a model of digital mammographic imaging systems. This model takes into account appropriate
physical image acquisition processes representing a particular digital X-ray mammography system. The X-ray
spectrum, local glandularity above the insertion site and scatter were all taken account during the insertion
procedure. A preliminary observer study was used to validate the realism of the masses. Seven expert readers each
viewed 60 full field mammograms and rated the realism of the masses they contained. Half of the images contained
real, histologically-confirmed masses, and half contained simulated lesions. The ROC analysis of the study (average
AUC of 0.58±0.06) suggests that, on the average, there is evidence that the radiologists could distinguish, somewhat,
between real and simulated masses.
A method to convert digital mammograms acquired on one system to appear as if acquired using another system is
presented. This method could be used to compare the clinical efficacy of different systems. The signal transfer properties
modulation transfer function (MTF) and noise power spectra (NPS) were measured for two detectors - a computed
radiography (CR) system and a digital radiography (DR) system. The contributions to the NPS from electronic, quantum
and structure sources were calculated by fitting a polynomial at each spatial frequency across the NPS at each dose. The
conversion process blurs the original image with the ratio of the MTFs in frequency space. Noise with the correct
magnitude and spatial frequency was added to account for differences in the detector response and dose. The method was
tested on images of a CDMAM test object acquired on the two systems at two dose levels. The highest dose images were
converted to lower dose images for the same detector, then images from the DR system were converted to appear as if
acquired at a similar dose using CR. Contrast detail curves using simulated CDMAM images closely matched those of
CDMAM phantoms are widely used in the Europe to assess the performance of mammography systems
utilising small size and low contrast disc details. However, the assessment of CDMAM images by human
observers is slow and tedious. An automated method for scoring CDMAM images (CDCOM) is widely
available to address this issue. We have developed an alternative automated scoring tool to score CDMAM
images, Quantitative Assessment System (QAS), for similarly removing inter- and intra- observer variability.
This provides additional valuable information about the contrast and SNR of each gold disc within the
image. The QAS scores CDMAM phantom images using a scanning algorithm. QAS scoring results were
compared with human observers and with CDCOM. It was found that QAS was comparable with human
observers in scoring, whereas CDCOM consistently scored a higher number of discs correctly in CDMAM
images compared with QAS and human observers.
QAS results have been used to analyse the effects of different digital mammography system modulation
transfer functions (MTFs) on fine details for a number of systems in the form of contrast degradation factor
(CDF) measurements. CDF curves for experimentally acquired CDMAM images were compared with those
for simulated CDMAM images to assess the accuracy of contrast measurements.