The use of local entropy measures in edge detection for cytological image analysis

Accurate edge detection is a fundamental problem in the areas of image processing and pattern recognition/classification. The lack of effective edge detection methods has slowed the application of image processing to many areas, in particular diagnostic cytology, and is a major factor in the lack of acceptance of image processing in service orientated pathology. In this paper, we present a two-step procedure which detects edges. Since most images are corrupted by noise and often contain artefacts, the first step is to clean up the image. Our approach is to use a median filter to reduce noise and background artefacts. The second operation is to locate image pixels which are 'information rich' by using local statistics. This step locates the regions of the image most likely to contain edges. The application of a threshold can then pin-point those pixels forming the edge of structures of interest. The procedure has been tested on routine cytologic specimens.