An Adaptive Clustering Algorithm of Gaussian Curve Fitting the Histogram Based on K-Means
Guangkun Ma, Jinzhu Yang, Xiaoxu Song, Hong Liang Zhao · 2019
The K-mean algorithm is simple and efficient in vascular segmentation. But in a K-means clustering algorithm, the number of clustering centers must be determined in advance and the random allocation of clustering centers seriously affects the efficiency of the algorithm. In order to solve this problem, an adaptive clustering algorithm based on K-means with the Gaussian curve fitting histogram is proposed, which is used to segment circular-like blood vessels. Firstly, the histogram outline is fitted into the Gaussian curve by the least square method and the maximum value of the curve is obtained. The number of maximal values is counted as the number of clustering centers and the maximum points are used as the central points of clustering. Also, the K-means algorithm is improved by combining the intensity and position information of pixels. The proposed method has been tested by 6 groups of medical images. The algorithm has the advantages of less iterations, fast convergence speed and getting more than 95% Dice Similarity Measure value. The algorithm can effectively solve the determination of multi-seed points and vascular segmentation in medical image segmentation.