On the use of multifractal analysis and genetic algorithms for the segmentation of cervical cell images
This paper deals with the segmentation problem of cervical cell images. This segmentation consists of separating each cell into its core and its cytoplasm. The mentioned separation is part of a uterus cancer recognition system based on the morphology of both the cell core and the cell cytoplasm. In this paper, we propose to perform the above separation by using a multifractal algorithm. An important feature of the proposed algorithm is its low computation cost. To increase the quality of the segmentation, we propose an optimization step based on genetic algorithms. The proposed processing has been tested on several images. Herein, we present only the results obtained for three different cell images.
genetic algorithms cell images segmentation in homogeneous areas unsupervised multifractal segmentation singularity exponent