Objective: We investigated an algorithm to detect grey level transitions with multiple scales of resolution to improve edge detection and localisation in ultrasound images of the prostate.
Introduction: We had developed a non-analytical operator for prostate contour determination implemented with minimum and maximum filters and locate edges. We implemented a technique for improved determination of boundary parts in prostatic ultrasound images by adjusting the edge detection parameter to signal information.
Methods: First the influence of prefilter settings and edge detection parameters is investigated in a test image and a real ultrasound image. Then, local standard deviation is used to identify or fewer homogeneous regions that are filtered with course resolution, while areas with larger deviation that grey level transitions occur, which should be preserved using smaller filter sizes to improve edge localisation.
Results: Analysis of images with different filter sizes indicated that areas are merged for increasing filter sizes: less pronounced edges disappear or displace for larger filters. Two scales of resolution lead to an improved localisation of edges when smaller filter sizes are used in areas with an increased local standard deviation.
Conclusions: This paper illustrates an edge 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 modalities. Also, disadvantages are discussed, resulting in a new application combining a localisation algorithm to find the initial contour and a delineation algorithm to improve the outlining of the resulting contour.