Probabilistic model based on separating bipoints to segment multithresholdable images
Aline Deruyver, Yann Hodé · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
Because of noise, edge detection seldom gives the whole contour of objects in images. We developed a new method to better extract information provided by partial edge detection in order to segment multi-thresholdable images. It consists in looking for separating bipoints corresponding to the normals to the most striking boundaries. The thresholds take their values within the intervals defined by these bipoints. The probabilistic model proposed in this paper is not dependent on the distribution on pixel values and allows to determine the different family of intervals corresponding to a threshold domain. This method was tested with success on positron Emission Tomography images and on a set of 4000 fluorescence images. It demonstrates a good efficiency despite the low contrast and high blurring of such images.