Using gray-levels and morphological features for image segmentation

A new segmentation algorithm is suggested, which is based on iterated thresholding and on morphological features. The histogram of the grey-level image is used to identify two initial global thresholds ?1 and ?2, used to assign to the foreground and to the background respectively, pixels with grey-level below ?1 and above ?2. Local thresholding is then accomplished for each component of pixels that have not been assigned to any of the two sets by the global thresholding process. For each component, a new pair of thresholds is detected on the relative histogram. Local thresholding is applied to the components of undecided pixels as far as the relative histogram presents valleys and peaks. Then, to assign to the foreground or to the background the still undecided sets of pixels, morphological features are used. The suggested segmentation method works well for images, like many biological images, where the foreground is perceived as locally darker (or locally lighter) than the background, consistently through the whole image, and performs better than segmentation based on simple global thresholding.