Open AccessA Fast Density Peak Clustering Algorithm Optimized by Uncertain Number Neighbors for Breast MR Image
Author(s) -
Hong Fan,
Jing Yang,
Hou Cun-cun,
Kezhen Zhang,
Ruoxia Yao
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1229/1/012024
Subject(s) - cluster analysis , artificial intelligence , pattern recognition (psychology) , segmentation , image segmentation , pixel , computer science , image (mathematics) , boundary (topology) , algorithm , mathematics , computer vision , mathematical analysis
Aiming at the characteristics of abundant Magnetic Resonance (MR) image information, uneven gray scale, fuzzy boundary, and a fine structure that is difficult to distinguish and segment, this paper proposes an algorithm for segmenting MR images by an improved density clustering algorithm. First, the superpixel clustering algorithm (SLIC) is used to divide the image into a certain number of ultra-pixel regions and subsequently search the neighborhood of each superpixel according to a pre-specified threshold. Then, the KNN-DPC algorithm, in which the value of K is adaptively determined, is used to obtain the distance information of K nearest neighbors (density) and adjacent superpixels, and image segmentation is completed for each superpixel cluster. Two sets of natural image experiments show that this algorithm has high segmentation accuracy. Experiments on clinical breast MR images showed that this algorithm achieved good results for clinical MR image segmentation.