Title
Local segmentation of images using an improved fuzzy C-means clustering algorithm based on self-adaptive dictionary learning
Abstract
Image segmentation is an active research topic in image processing. The Fuzzy C-means (FCM) clustering analysis has been widely used in image segmentation. As there is a large amount of delicate tissues such as blood vessels and nerves in medical images, noise generated during imaging process can easily affect successful segmentation of these tissues. The traditional FCM algorithm is not ideal for segmentation of images containing strong noise. In this study, we proposed an improved FCM algorithm with anti-noise capability. We first discussed the algorithm of dictionary learning for noise reduction. Then we developed a new image segmentation algorithm as a combination of the dictionary learning for noise reduction and the improved fuzzy C-means clustering. Lastly we used the algorithm of the improved FCM to segment images, during which we removed the non-target areas making use of the grayscale features of images and extracted accurately the areas of interests. The algorithm was tested using synthetic Shepp-Logan images and real medical magnetic resonance imaging (MRI) and computed tomography (CT) images. Compared to the synthetic data and real medical images segmented by the fuzzy C-means (FCM) clustering algorithm, the Kernel Fuzzy C-mean (KFCM) clustering algorithm, spectral clustering algorithm, the sparse learning based fuzzy C-means (SL_FCM) clustering algorithm, and the modified spatial KFCM (MSFCM) algorithm, the images segmented by the dictionary learning Fuzzy C-mean clustering (DLFCM) algorithm have higher partition coefficient, lower partition entropy, better visual perception, better clustering accuracy, and clustering purity.
Year
DOI
Venue
2020
10.1016/j.asoc.2020.106200
Applied Soft Computing
Keywords
DocType
Volume
Dictionary learning,Fuzzy C-means clustering,Algorithm of the noise reduction,Image segmentation,MRI and CT
Journal
91
ISSN
Citations 
PageRank 
1568-4946
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Jiaqing Miao171.45
Xiaobing Zhou200.34
T. Z. Huang311518.95