New neutrosophic approach to image segmentation

被引:179
作者
Guo, Yanhui [1 ,2 ]
Cheng, H. D. [1 ,2 ]
机构
[1] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China
[2] Utah State Univ, Dept Comp Sci, Logan, UT 84322 USA
关键词
Neutrosophic set; Entropy; Image segmentation; Indeterminacy; gamma-means clustering; FUZZY-SETS; ALGORITHM;
D O I
10.1016/j.patcog.2008.10.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Neutrosophic set (NS), a part of neutrosophy theory, Studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. NS is a formal framework that has been recently proposed. However, NS needs to be specified from a technical point of view for a given application or field. We apply NS, after defining some concepts and operations, for image segmentation. The image is transformed into the NS domain, which is described using three membership sets: T, I and F. The entropy in NS is defined and employed to evaluate the indeterminacy. Two operations, alpha-mean and beta-enhancement operations are proposed to reduce the set indeterminacy. Finally, the proposed method is employed to perform image segmentation using a gamma-means clustering. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment the images automatically and effectively. Especially, it can segment the "clean" images and the images having noise with different noise levels. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码:587 / 595
页数:9
相关论文
共 17 条
[1]  
Anderberg M.R., 1973, Probability and Mathematical Statistics, DOI DOI 10.1016/C2013-0-06161-0
[2]  
[Anonymous], 1981, PATTERN RECOGNITION
[3]   Threshold selection using fuzzy set theory [J].
Chaira, T ;
Ray, AK .
PATTERN RECOGNITION LETTERS, 2004, 25 (08) :865-874
[4]   A NEW NEUTROSOPHIC APPROACH TO IMAGE THRESHOLDING [J].
Cheng, H. D. ;
Guo, Yanhui .
NEW MATHEMATICS AND NATURAL COMPUTATION, 2008, 4 (03) :291-308
[5]   Color image segmentation: advances and prospects [J].
Cheng, HD ;
Jiang, XH ;
Sun, Y ;
Wang, JL .
PATTERN RECOGNITION, 2001, 34 (12) :2259-2281
[6]   A clustering fuzzy approach for image segmentation [J].
Cinque, L ;
Foresti, G ;
Lombardi, L .
PATTERN RECOGNITION, 2004, 37 (09) :1797-1807
[7]  
Duda R.O., 1973, Pattern Classification and Scene Analysis
[8]  
GUO Y, 2009, NEW MATH NA IN PRESS
[9]   A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns [J].
Ma, Li ;
Staunton, R. C. .
PATTERN RECOGNITION, 2007, 40 (11) :3005-3011
[10]   THRESHOLD SELECTION METHOD FROM GRAY-LEVEL HISTOGRAMS [J].
OTSU, N .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1979, 9 (01) :62-66