Abstract
In this decade, various superpixel-based techniques have been proposed by various authors for image segmentation where few of them are for medical images. These algorithms are evaluated using several evaluation measures and datasets, resulting in inconsistency in algorithm comparison. We noticed that some superpixel-based algorithms are performing better than other algorithms, for example, clustering-based superpixel methods are more efficient than graph-based superpixel techniques. In this paper, we choose simple linear iterative clustering (SLIC) for its less computation time and great performance on pathology images. In this paper, particle swarm optimization (PSO) and k-means clustering (KM) methods are used both with superpixel preprocessing for kidney renal cell carcinoma images. Clustered images are compared with ground truth images, and SLIC with PSO outperformed all other methods.
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This work has been partially supported by the grant received in the research project under RUSA 2.0 component 8, Govt. of India, New Delhi.
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Ray, S., Dhal, K.G., Naskar, P.K. (2023). Superpixel Image Clustering Using Particle Swarm Optimizer for Nucleus Segmentation. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_34
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DOI: https://doi.org/10.1007/978-981-19-6525-8_34
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