Histopathological cells segmentation using exponential grasshopper optimisation algorithm-based fuzzy clustering method. (25th August 2020)

• Record Type: Journal Article
• Authors: Tiwari, Varun; Jain, S.C.
• Is Part Of: International journal of intelligent information and database systems. Volume 13:Number 2/4(2020)
• Page Start: 118
• Page End: 138
• Publication Date: 2020-08-25
• Subjects: histopathological images -- cell segmentation -- nature-inspired algorithm -- grasshopper optimisation algorithm -- GOA; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

Automated cell segmentation in histopathological images is a challenging problem due to the complexities of these images. In this paper, a new exponential grasshopper optimisation algorithm is presented which is further used to find the optimal fuzzy clusters for segmenting the cells in histopathological images. For better cluster quality, compactness is considered as the objective function. The performance of the proposed method is validated in terms of F1 score and aggregated Jaccard index value on two standard histopathological image datasets, namely TNBC patients cancer dataset and UCSB bio segmentation images dataset. The simulation results show the effectiveness of the proposed method over other state-of-the-art clustering segmentation methods such as K-means and fuzzy c-means.