A. Castro, C. Bveda, and B. Arcay (Spain)
Fuzzy Clustering; Segmentation; Fuzzy C-Means; Cluster
Most clustering algorithms require two parameters: the
number of clusters and a sample of each cluster.
These parameters are critical, because their value
determines the convergence speed of the algorithm and
the obtained result. The technique that is most often used
to calculate these values is the application of validity
indices that suggest the best amount of clusters for the
division of the image.
Our group proposes an algorithm for the determination of
the above parameters on the basis of the analysis of the
histogram. The algorithm starts by smoothening the
histogram, it then calculates the various valleys and peaks
and on that basis the area and the distance between the
different modes, and finally it uses these two parameters
to provide the number of clusters and a sample of each.
The results of testing various medical images show that
the algorithm is able to divide the image into a correct
number of clusters for its analysis, and that the provided
centroids make the algorithm merge rapidly towards a