With the emergence of recent technology in breast ultrasound, sonographic image
quality has changed profoundly. Most notably, the technique of real-time
spatial compounding impacts the appearance of lesions and parenchyma. During
image acquisition, spatial compounding can be turned on or off at the
discretion of the radiologist, but this information is not stored along with
the image data.
The ability to distinguishing between lesions imaged with and without spatial
compounding, using either single image features or a Bayesian neural net (BNN), was assessed using ROC analysis. Our database consisted of consecutively
collected HDI5000 images of 129 lesions imaged without spatial compounding
(357 images, cancer prevalence of 18%) and 370 lesions imaged with spatial
compounding (965 images, cancer prevalence 15%). These were used in automated
feature selection and BNN training. An additional 33 lesions were imaged for
which identical views with and without spatial compounding were available (70
images, cancer prevalence 15%). These served as an independent test dataset.
Lesions were outlined by a radiologist and image features, mathematically
describing lesion characteristics, were calculated.
In feature selection, the 4 best performing features were related to gradient
strength and entropy. The average gradient strength within a lesion obtained an
area under the ROC curve (AUC) of 0.78 in the task of distinguishing lesions
imaged with and without spatial compounding. The BNN, using 4 features,
achieved an AUC on the independent test dataset of 0.98 in this task.
The sonographic appearance of breast lesions is affected by spatial compound
imaging and lesion features may be used to automatically separate images as
obtained with or without this technique. In computer-aided diagnosis (CADx),
it will likely be beneficial
to separate images as such before using separate classifiers for assessment of