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Microscopic image segmentation approach based on modified affinity propagation-based clustering

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Abstract

Microscopic image analysis is an important task from the diagnostic point of view because microscopic investigation is often required to diagnose the root cause of some diseases. Some inherent characteristics of the microscopic images often create some problems for the automated analysis algorithms and therefore, a lot of developments are to be performed to enrich the automated and computer-aided diagnostic systems. The proposed work proposes a novel microscopic image segmentation approach that is based on affinity propagation-based clustering. The main objective of this work is to provide an elegant solution for computer-aided diagnostic systems by introducing an efficient segmentation approach because segmentation plays a crucial role in many biomedical image analysis frameworks. A balanced semi-supervised framework is proposed that can work with a lesser number of annotations. The proposed approach uses incremental and decremental learning besides affinity propagation clustering. It allows a balance between new learning and forgetting already learned information. The proposed affinity calculation method is used for clustering purposes. The proposed approach can be helpful for real-life microscopic image interpretation purposes and can act as a third eye for physicians. The proposed approach can be helpful where properly annotated ground truth segmented data are not available. Some real-life microscopic data are considered to perform the experiments and the obtained results are quite encouraging and prove the effectiveness of the proposed approach.

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Acknowledgements

The authors would like to express their gratitude and thank the anonymous reviewers and referees for their precious comments and suggestions which are helpful in further improvement of the research work.

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  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, India

    Shouvik Chakraborty & Kalyani Mali

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  1. Shouvik Chakraborty
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  2. Kalyani Mali
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Correspondence to Shouvik Chakraborty.

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Chakraborty, S., Mali, K. Microscopic image segmentation approach based on modified affinity propagation-based clustering. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18486-4

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