Evaluation of Calinski-Harabasz Criterion as Fitness Measure for Genetic Algorithm Based Segmentation of Cervical Cell Nuclei

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Caglar Cengizler
M. Kerem Un

Abstract

In this paper, the classification capability of Calinski-Harabasz criterion as an internal cluster validation measure has been evaluated for clustering-based region discrimination on cervical cells. In this approach, subregions in the sample image are initially randomly constructed to be the individuals of the population. At each generation, individuals are evaluated according to their Accordingly a novel genetic structure for meta heuristic area isolation is proposed. Evaluation of proposed combination of genetic algorithm and Calinski-Harabasz measure is achieved by experiments, conducted on real cervical cell samples. We have used two separate cluster validity measures to evaluate the performance of the clustering approach. Jaccard index and F-score are utilized for objective comparison. Results shows that, Calinski-Harabasz criteria may have a better performance with proposed novel genetic structure and presented mechanism may have great potential on discrimination of specific regions.

Keywords:
Cervical, nuclei, segmentation, evolutionary, Calinski-Harabasz, Davies-Bouldin

Article Details

How to Cite
Cengizler, C., & Un, M. K. (2017). Evaluation of Calinski-Harabasz Criterion as Fitness Measure for Genetic Algorithm Based Segmentation of Cervical Cell Nuclei. Journal of Advances in Mathematics and Computer Science, 22(6), 1-13. https://doi.org/10.9734/BJMCS/2017/33729
Section
Original Research Article