Abstract
In this paper, an unsupervised multiresolution image segmentation algorithm is put forward, which combines wavelet transform and improved fuzzy c-means clustering (FCM) considering neighboring pixels. In the first phase, the traditional FCM is applied to low-resolution image to get the initial image segmentation; Then, according to the properties of intrascale clustering and interscale persistence of wavelet coefficients, attach labels to image elements from coarse to fine scale; In the second phase, an improved FCM based on the neighboring pixels and obtained label of fine-scale, will be adopted for final image segmentation. In the experiments, medical images are segmented, which demonstrates the proposed method greatly restrains the influence of noise and shows good performance in the real medical images.
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Shi, Z., Liu, Y., Li, Q. (2013). Medical Image Segmentation Based on FCM and Wavelets. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_36
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DOI: https://doi.org/10.1007/978-3-642-42057-3_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
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