Abstract
An approach to automatic prediction and detection of ovulation is described. It is based on the application of image processing techniques to the cervical mucus fern test, a popular clinical diagnostic method. The sequence of histogram equalization, filtering, edge detection, binarization, labeling, thinning, Hough transform, and automatic pattern recognition in a feature space is applied to microscopic images of the ferning patterns. This method permits decisions to be made based on quantitative data instead of the subjective evaluations that are presently used. >
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