Objective: We investigated an algorithm to detect grey level transitio
ns with multiple scales of resolution to improve edge detection and lo
calisation in ultrasound images of the prostate. Introduction: We had
developed a non-analytical operator for prostate contour determination
implemented with minimum and maximum filters to identify and locate e
dges. We implemented a technique for improved determination of boundar
y parts in prostatic ultrasound images by adjusting the edge detection
parameter to signal information. Methods: First the influence of pref
ilter settings and edge detection parameters is investigated in a test
image and a real ultrasound image. Then, local standard deviation is
used to identify more or fewer homogeneous regions that are filtered w
ith course resolution, while areas with larger deviation indicate that
grey level transitions occur, which should be preserved using smaller
filter sizes to improve edge localisation. Results: Analysis of image
s with different filter sizes indicated that areas are merged for incr
easing filter sizes: less pronounced edges disappear or displace for l
arger filters. Two scales of resolution lead to an improved localisati
on of edges when smaller filter sizes are used in areas with an increa
sed local standard deviation. Conclusions: This paper illustrates an e
dge detection method suitable as pre-processing step in interpretation
of medical images. By adapting input parameters to signal information
, object recognition can be applied in images from different imaging m
odalities. Also. disadvantages are discussed, resulting in a new appli
cation combining a localisation algorithm to find the initial contour
and a delineation algorithm to improve the outlining of the resulting
contour. (C) 1998 Elsevier Science B.V.