Journal article Open Access

An Improved Method for Image Segmentation Using K-Means Clustering with Neutrosophic Logic

Mohammad Naved Qureshi; Mohd Vasim Ahamad

Images are one of the primary media for sharing information. The image segmentation is an important image processing approach, which analyzes what is inside the image. Image segmentation can be used in content-based image retrieval, image feature extraction, pattern recognition, etc. In this work, clustering based image segmentation method used and modified by
introducing neutrosophic logic. The clustering technique with neutrosophy is used to deal with indeterminacy factor of image pixels. The approach is to transform the image into the neutrosophic set by calculating truth, falsity and indeterminacy values of pixels and then, the clustering technique based on neutrosophic set is used for image segmentation. The clusters are then refined iteratively to make the image more suitable for the segmentation. This iterative process converges when required number of clusters areformed. Finally, the image in the neutrosophic domain is segmented. The proposed algorithm provides better results than existing K-means clustering approach.

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