Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm
TLDR
This paper presents k-means clustering algorithm, an unsupervised algorithm used to segment the interest area from the background, and subtractive cluster, a data clustering method, which generates the centroid based on the potential value of the data points.About:
This article is published in Procedia Computer Science.The article was published on 2015-01-01 and is currently open access. It has received 709 citations till now. The article focuses on the topics: Segmentation-based object categorization & Cluster analysis.read more
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References
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Current methods in medical image segmentation.
TL;DR: A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented, with an emphasis on the advantages and disadvantages of these methods for medical imaging applications.
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Generation of Fuzzy Rules by Mountain Clustering
TL;DR: This work develops, based upon the mountain clustering method, a procedure for learning fuzzy systems models from data, and uses a back propagation algorithm to tune the model.
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Cluster center initialization algorithm for K -means clustering
Shehroz S. Khan,Amir Ahmad +1 more
TL;DR: An algorithm to compute initial cluster centers for K-means clustering based on two observations that some of the patterns are very similar to each other and that is why they have same cluster membership irrespective to the choice of initial cluster center.
A Survey of Current Methods in Medical Image Segmentation
TL;DR: A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented with an emphasis placed on revealing the advantages and disadvantages of these methods for medical imaging applications.
Improving the Accuracy and Efficiency of the k-means Clustering Algorithm
TL;DR: A method for making the k-means clustering algorithm more effective and efficient, so as to get better clustering with reduced complexity is proposed.