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- W3171010991 abstract "Spatial information with the fuzzy membership function plays an important role in the segment and classify the remote sensing images as well as medical images. In this paper, a Gaussian distribution-based spatial fuzzy c-means method has been proposed for the segmentation and classification of remote sensing images. To check the working principle of the proposed method in the interdisciplinary field, it has also been tested on brain MRI image. The intensity-based distances have been replaced with the compliment of Gaussian distribution to focus on the active artifacts in the datasets at the time of segmentation. The correlation of the neighbors has been estimated as local spatial membership, which is used to deal with the uncertainties and artifacts. The partition coefficient and partition entropy have been measured as quantitative statistical parameters and the segmented images as qualitative parameters, which have been used to understand the supremacy of the proposed method over the considered state of the art techniques.KeywordsArtifactsBrain MRI imageFuzzy c-meansIntensity inhomogeneityRemote sensingSegmentationSpatial information" @default.
- W3171010991 created "2021-06-22" @default.
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- W3171010991 date "2021-01-01" @default.
- W3171010991 modified "2023-09-25" @default.
- W3171010991 title "Gaussian-Based Spatial FCM Technique for Interdisciplinary Image Segmentation" @default.
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- W3171010991 doi "https://doi.org/10.1007/978-981-16-1543-6_27" @default.
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