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- W3012212461 abstract "In order to avoid K-means algorithm falling into local optimum solution, this paper proposes an image segmentation method based on peak-valley principle and K-means algorithm and apply it to the Computed Tomography(CT) image. Firstly, based on the observation of a large number of medical image gray histograms, peak-valley principle is summarized. Then, under the guidance of this principle, a K-means algorithm improved by Peak-Valley Principle (PVK-means) is proposed. Assuming that the number of clusters is K, PVK-means algorithm uses neighborhood valley-emphasis Otsu algorithm to select K-1 global thresholds according to the quantitative principle in peak-valley principle. According to the shape invariance principle, the adjacency principle and the maximum principle in the peak-valley principle, the maximum value in the interval is selected as the initial clustering centroid. Finally, K-means algorithm is carried out with the selected initial clustering centroid. The experimental results show that PVK-means algorithm can not only avoid K-means algorithm falling into local optimum solution, but also improve the segmentation efficiency by more than 25%." @default.
- W3012212461 created "2020-03-23" @default.
- W3012212461 creator A5059693690 @default.
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- W3012212461 date "2019-11-13" @default.
- W3012212461 modified "2023-09-24" @default.
- W3012212461 title "An Image Segmentation Method Based on Peak-valley Principle and K-means Algorithm" @default.
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- W3012212461 doi "https://doi.org/10.1145/3379299.3379304" @default.
- W3012212461 hasPublicationYear "2019" @default.
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