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21 July 1999 Probabilistic model based on separating bipoints to segment multithresholdable images
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Aline Deruyver and Yann Hode "Probabilistic model based on separating bipoints to segment multithresholdable images", Proc. SPIE 3716, Visual Information Processing VIII, (21 July 1999);


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