2020
DOI: 10.1109/tmi.2019.2956944
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Circular Clustering in Fuzzy Approximation Spaces for Color Normalization of Histological Images

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Cited by 11 publications
(7 citation statements)
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“…Maji et al. [113] presented a circular clustering algorithm to find the ‘centroid’, ‘a crisp lower approximation’, and the ‘fuzzy boundary’, which could be integrated by saturation-weighted hue histogram in the HIS colour space.…”
Section: Data Harmonisation Strategies For Information Fusionmentioning
confidence: 99%
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“…Maji et al. [113] presented a circular clustering algorithm to find the ‘centroid’, ‘a crisp lower approximation’, and the ‘fuzzy boundary’, which could be integrated by saturation-weighted hue histogram in the HIS colour space.…”
Section: Data Harmonisation Strategies For Information Fusionmentioning
confidence: 99%
“…Since the NMI does not consider the consistency of the ROI within the same biopsy set of S images, Maji et al. [113] presented an extension of NMI, named Between-Image colour constancy (BiCC) index, which can be represented by where and . The value of BiCC ranges from 0 to 1, an efficient harmonisation algorithm for image modality should make the value as high as possible.…”
Section: Evaluation Approaches Of the Data Harmonisation Strategiesmentioning
confidence: 99%
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“…The linguistic measurement of opinion can be solved using fuzzy methods in a cryptic form that can be weighted. To optimize the processing of opinion data so that it is effective and accurate, a text mining application for audience opinion is made to present traditional cultural artworks using fuzzy clustering (Maji and Mahapatra, 2020). Before the implementation stage is carried out, a testing phase is required.…”
Section: Introductionmentioning
confidence: 99%