Rn. Strickland et Hi. Hahn, WAVELET TRANSFORMS FOR DETECTING MICROCALCIFICATIONS IN MAMMOGRAMS, IEEE transactions on medical imaging, 15(2), 1996, pp. 218-229
Citations number
43
Categorie Soggetti
Engineering, Biomedical","Radiology,Nuclear Medicine & Medical Imaging
Clusters of fine, granular microcalcifications in mammograms may be an
early sign of disease. Individual grains are difficult to detect and
segment due to size and shape variability and because the background m
ammogram texture is typically inhomogeneous. We develop a two-stage me
thod based on wavelet transforms for detecting and segmenting calcific
ations. The first stage is based on an undecimated wavelet transform,
which is simply the conventional filter bank implementation without do
wnsampling, so that the low-low (LL), low-high (LH), high-low (HL), an
d high-high (HH) sub-bands remain at full size. Detection takes place
in HH and the combination LH+HL. Four octaves are computed with two in
ter-octave voices for finer scale resolution. By appropriate selection
of the wavelet basis the detection of microcalcifications in the rele
vant size range can be nearly optimized. In fact, the filters which tr
ansform the input image into HH and LH+HL are closely related to prewh
itening matched filters for detecting Gaussian objects (idealized micr
ocalcifications) in two common forms of Markov (background) noise. The
second stage is designed to overcome the limitations of the simplisti
c Gaussian assumption and provides an accurate segmentation of calcifi
cation boundaries. Detected pixel sites in HH and LH+HL are dilated th
en weighted before computing the inverse wavelet transform, Individual
microcalcifications are greatly enhanced in the output image, to the
point where straightforward thresholding can be applied to segment the
m, FROG curves are computed from tests using a freely distributed data
base of digitized mammograms.