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A Nuclei Segmentation Method Based on Optimal Fuzzy Clustering Using Salp Swarm Algorithm for Histopathological Images

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ICDSMLA 2019

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 601))

Abstract

Automatic segmentation of nuclei in the H&E stained histopathological images is an open-ended research problem. In this paper, a new salp swarm algorithm based fuzzy clustering method is proposed which is used for nuclei segmentation in the histopathological images. The salp swarm algorithm finds the optimal clusters by the objective function defined over intra-cluster distances or compactness. The performance of the proposed segmentation method is evaluated in terms of F1 score and aggregated jaccard index on the histopathological image dataset of TNBC patients. The experimental results depict the efficacy of the proposed method over the other considered clustering-based segmentation methods, namely K-means and Fuzzy C-means.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

    Venubabu Rachapudi

  2. Department of Computer Science and Systems Engineering, AUCE(A), Andhra University, Visakhapatnam, India

    G. Lavanya Devi & Ramesh Neelapu

Authors
  1. Venubabu Rachapudi
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  2. G. Lavanya Devi
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  3. Ramesh Neelapu
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Corresponding author

Correspondence to Venubabu Rachapudi .

Editor information

Editors and Affiliations

  1. BioAxis DNA Research Centre Private Ltd, Hyderabad, Telangana, India

    Amit Kumar

  2. Polish Academy of Science, Systems Research Institute, Warsaw, Poland

    Marcin Paprzycki

  3. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, Telangana, India

    Vinit Kumar Gunjan

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Rachapudi, V., Lavanya Devi, G., Neelapu, R. (2020). A Nuclei Segmentation Method Based on Optimal Fuzzy Clustering Using Salp Swarm Algorithm for Histopathological Images. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_190

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