|Table of Contents|

Ant Colony Algorithm and Density Peaks Clustering forMedical Image Segmentation(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年02期
Page:
1-8
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
Ant Colony Algorithm and Density Peaks Clustering forMedical Image Segmentation
Author(s):
Zhu Hong1; He Hanzhi1; Fang Qianhao1; Dai Yue2; Jiang Daihong3
(1.School of Medical Information,Xuzhou Medical University,Xuzhou 221004,China)(2.Radiology Department,The Affiliated Hospital of Xuzhou Medical University,Xuzhou 221002,China)(3.School of Information and Electrical Engineering,Xuzhou Institute of Technology,Xuzhou 221008,China)
Keywords:
medical image segmentation; density peaks; cluster centers; ant colony algorithm; pheromone
PACS:
TP18
DOI:
10.3969/j.issn.1001-4616.2019.02.001
Abstract:
In the medical image segmentation research,a medical image segmentation method based on ant colony algorithm for selecting the optimal parameters of density peaks clustering(DPC)was proposed. For some defects in DPC algorithm,such as cut-off distance dc was given by DPC algorithm relied on prior knowledge,subjective randomness in cluster centers was selected by manual work. First,the algorithm took advantages of the overall robustness of the ant colony algorithm,used image entropy to calculate pheromone to guide the search path of ant colony. Then it was quantified the number of cluster centers by using variable quantification instead,and ant colony selected the optimal truncation distance dc and cluster centers by iteration,and realized the adaptive segmentation of DPC algorithm and obtained better results. Simulation experiments proved the effectiveness and practicability of this algorithm.

References:

[1] SHRIVASTAVA K,GUPTA N,SHARMA N. Medical image segmentation using modified K-means clustering[J]. Journal of gerontological nursing,2014,24(1):54.
[2]YADAV H,BANSAL P,SUNKARIA R K. Color dependent K-means clustering for color image segmentation of colored medical images[C]//2015 First International Conference on Next Generation Computing Technologies. Dehardun,India:IEEE,2016:858-862.
[3]ZHOU N,YANG T,ZHANG S. An improved FCM medical image segmentation algorithm based on MMTD[J]. Computational and mathematical methods in medicine,2014(1):690349.
[4]唐文静,许兆新,张小峰. 峰值检测FCM算法的医学图像分割[J]. 智能系统学报,2014(5):584-589.
[5]ZHU H,XU J,HU J,et al. Medical image segmentation using improved affinity propagation[C]//Fifth International Symposium Computational Modeling of Objects Represented in Images. Niagrara Falls NY,USA:Springer,Cham,2016:208-215.
[6]RODRIGUEZ A,LAIO A. Clustering by fast search and find of density peaks[J]. Science,2014,344(6191):1492-1496.
[7]杨光. 基于密度峰值聚类的前列腺KVCT图像3D分割[D]. 西安:西安电子科技大学,2015.
[8]杨悦,刘向阳. 基于局部空间密度峰值的图像分割算法[J]. 信息技术,2017(9):84-87.
[9]王泽,张宏军,张睿,等. 基于遗传算法与密度及距离计算的聚类方法[J]. 计算机应用,2015,35(11):3243-3246.
[10]DENG S L,CHEN W D. K-means clustering for color image segmentation of colored medical images[C]//2015 First International Conference on Next Generation Computing Technologies. Dehardun,India:IEEE,2016:858-862.
[11]杨志,罗可. 一种改进的基于粒子群的聚类算法[J]. 计算机应用研究,2014,31(9):2597-2599.
[12]ZHANG Z,YUE B,PAN Q,et al. Image segmentation algorithm of fuzzy clustering based on ant colony and adaptive filtering[J]. Application of electronic technique,2015,41(4):144-147.
[13]YANG J,TAN W,MA S,et al. Automatic MRI brain tissue extraction algorithm based on three-dimensional gray-scale transformation model[J]. Journal of medical imaging and health informatics,2014,4(6):907-911.
[14]JENG J H,HSU S L,CHANG Y. Entropy improvement for fractal image coder[J]. International Arab journal of information technology,2012,9(5):403-410.
[15]GUI Y,LIN D,LIANG S,et al. A coarse-to-fine horizon detection method based on between-class variance[C]//Ninth International Symposium on Computational Intelligence and Design. Hangzhou,China:IEEE,2017:376-380.

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Last Update: 2019-06-30