43.an Image Segmentation Algorithm Based on Fuzzy c Means Clustering

7/31/2019 43.an Image Segmentation Algorithm Based on Fuzzy c Means Clustering

1/5

AAAAnnnn ImageImageImageImage SegmentationSegmentationSegmentationSegmentationAAAAlgorithmlgorithmlgorithmlgorithm

BasedBasedBasedBased ononononFFFFuzzyuzzyuzzyuzzyCCCC-M-M-M-Meanseanseanseans ClusteringClusteringClusteringClustering

Xin-bo ZHANG

College of Information and Electronic

Engineering,ZheJiang Gongshang

University

Hangzhou,zhejiang,china

AAAAbstractbstractbstractbstract

Image segmentation algorithm based on fuzzy c-

means clustering is an important algorithm in

the image segmentation field . It has been used

widely. However, it is not successfully to segment

the noise image because the algorithm

disregards of special constraint information. It

only considers the gray information. Therefore ,

we proposed a weighed fcm algorithm based on

gaussian kernel function for image segmentation .

The original euclidean distance is replaced by a

kernel-induced distance in the algorithm. Then,

a bound term is added to the objective function

to compensate the influence of the spatial

information. The experimental results illustrate

that the p roposed method is more effective to

image segmentation.

KKKKeyeyeyey words:words:words:words: fuzzy c-means; gaussian kernel

function; weighted; image segmentation

1.1.1.1. IntroductionIntroductionIntroductionIntroduction

Image segmentation algorithm is a crucial step

in the image processes and analysis. It plays a

vital role in many fiel ds such as computer vision,

pattern recognition and medical image

processing. The goal of segmentation is to

separate an image into some regions of feature

and to pick up the interesting objects. Although ,

Li JIANGCollege of Information and Electronic

Engineering ,ZheJiang GongshangUniversity

Hangzhou,zhejiang,china

[email protected]

lots of work have been done, there is still no a

general segmentation algorithm and a

impersonality criterion to estimate thesegmentation. According to concretely instances,

many segmentation algorithms have been put

forward. Among them, image segmentation

algorithm based on fuzzy c-means is an

important algorithm in the image segmentation

field[10][12]. I t coul d r etai n m uch m ore

information from the original image than hard

segmentation m ethods . Fuzzy c-means clustering

algorithm is a representative clustering algorithm.

However, FCM algorithm can't realiz e feature

optimiz ation of the sample dates. Meanwhile,

with the original FCM algorithm, we can't get

satisfactory results because of the i nfluence of

the noise.

Document[2][4][7][9] put forward a FCM

algorithm based on kernel density function. The

original euclidean distance is replaced by a

kernel-induced distance in the algorithm. This

new algorithm can effectively analyz e many data

structures such as non-hyperspherical structure,

mixed structure composed of heterogeneous

cluster prototypes , and data with noise and so on .

In this paper, using kernel function instead of

euclidean distance, considering spatial

information, initializi ng weight and increasing

constraint item, we proposed a weighed fcm

algorithm based on gaussian kernel function for

image segmentation . The proposed method can

International Conference on Digital Image Processing

978-0-7695-3565-4/09 $25.00 2009 IEEE

DOI 10.1109/ICDIP.2009.15

22


2/5


3/5


4/5

(10)( )

( )

=

==N

j

m

ijj

N

jj

m

ijj

i

u

xuv

1

1

5.5.5.5. ExperimentsExperimentsExperimentsExperiments andandandand resultsresultsresultsresults

In order to compare the image segmentation

property of the traditional FCM algorithm and

the proposed algorithm, we make lots of

experiments on different images and analyz e the

results.

Experiment one carries out division on a

car license image containing many targets.

Figure 1(a) is a primitive image. We make

division on it by FCM algorithm, KFCM

algorithm and KWFCM algorithm respectively.

FigureFigureFigureFigure 1(a).1(a).1(a).1(a). TheTheTheThe primitiv eprimitiveprimitiveprimitive imageimageimageimage

FigureFigureFigureFigure 1(b).1(b).1(b).1(b).thethethethe imageimageimageimage divideddivideddivideddivided bybybybyFCMFCMFCMFCM

Those from figure 1(b) to figure 1(d) are the

images after division. Contrasting the

experimental results, it can be seen that the

background after diving includes massive noises

and the target is not clear although the FCM

algorithm can separate the goal from the

background. However, with the modified

algorithm which considers the influence of

neighbourhood pixels we get good division

effect.It has demonstrated the good anti-chirp

performance.

FigureFigureFigureFigure 1(c)1(c)1(c)1(c) .... thethethethe imageimageimageimage div ideddiv ideddiv ideddivided bybyb ybyKFCMKFCMKFCMKFCM

FigureFigureFigureFigure 1(d).1(d).1(d).1(d). thethethethe imageimageimageimage d ivideddivideddivideddivided bybyb ybyKWFCMKWFCMKWFCMKWFCM

Experiment two carries out division on a

image of lena.Figure 2(a) is a primitive image.

Figure 2 (b) and figure 2(c) are the images after

division by FCM algorithm and KWFCM

algorithm respectively. After diving by FCM

algorithm the mirror, pillar and the ri bbon on hat

can not be distinguished well. Detail parts such

as hair and cap ear cannot be distinguished nearly .

While these can be distinguished well by

KWFCM algorithm.

FigureFigureFigureFigure 2(a).2(a).2(a).2(a). TheTheTheThe primitiv eprimitiveprimitiveprimitive imageimageimageimage

FigureFigureFigureFigure 2(b).2(b).2(b).2(b). thethethethe imageimageimageimage afterafterafterafter divisiondivisiond ivisiondivision byb ybyb y

FCMFCMFCMFCM algorithmalgorithmalgorithmalgorithm

25


5/5

FigureFigureFigureFigure 2(c).2(c).2(c).2(c). thethethethe imageimageimageimage afterafterafterafter d iv isiondivisiondiv isiondivision byb yb yb y

KWFCMKWFCMKWFCMKWFCM algorithmalgorithmalgorithmalgorithm

6.6.6.6. ConclusionConclusionConclusionConclusion

Image segmentation is an important aspect in

image processing. The division quality directly

effect the high-level image analysis andunderstanding. That,using FCM algorithm in

image segmentation, can reduce the artificial

intervention effectively. It suits the images with

uncertainty and fuzziness. Since the tradition

FCM algorithm failed to consider the influence

of neighbourhood pixels,its anti-noise ability is

pretty bad.

This paper proposed a weighed fcm al gorithm

based on gaussian kernel function for image

segmentation . This method introduced theinfluence of neighbourhood pixels and kernel

function, initializ ed weight by 2D histogram and

perfected the division model.The experimental

results indicated that the proposed method is

quite effective. It has strong robustness to the

noise and certain actual meaning.

7.7.7.7. ReferencesReferencesReferencesReferences

[1] Bezdek J C, Pattern Recognition with Fuzzy

ObjectiveF unctionA lgorithms, Plenum Press, New

York, 1981.

[2] Dao-Qiang Zhang and Song-Can Chen, A novel

kernelized fuzzy C-means algorithm with

application in medical image segmentation,

Artificial Intelligence in Medicine, 2004,

PP.3750.

[3] Xin-bo GAO, Fuzzy Cluster Analysis and its

Applications, Publishing House of XIDIAN

University, Xian, 2004.1.

[4] Zhang DQ and Chen SC, Kernel-based fuzzy

clustering incorporating spatial constraints for

image segmentation, Proceedings of the Second

International Conference on Machine Learning

and Cybernetics,Xian,2003.11.

[5] R. J. Hathway and J. C. Bezdek, Optimiz ation of

clustering criteria by reformulation , IEEE

Transactions on Fuzz y System,May 1995, vol. 3,

no. 2, pp.241-245.

[6] Refael C.Gonzalez and Richard E.Woods,Digital

Image procc essing(second editi on), Pearson

Education Asia LimitedAnd Publishing House of

ElectronicsIndustyt, 2007.8.

[7] M. Girolami, Mercer kernel-based clustering in

feature space , IEEE Trans. Neual Networks,

May 2002, vol. 13, pp. 780-784.

[8] Zhang DQ, Chen SC. Fuzz y clustering using

kernel methods. In: Proceedings of the

International Conference on Controland

Automation, Xiamen,China, June,2002.

[9] Songcan Chen and Daoqiang Z hang, Robust

image segmentation using FCM with spatial

constraints based on new kernel-induced distance

measure, Systems, Man and Cybernetics, Part B,

IEEE Transactions on V olume34, Issue 4, Aug.

2004 Page(s):1907 - 1916.

[10] Bezdek JC, Hall LO and Clarke LP, Review of

MR i mage segmentation techniques using pattern

recognition, MedPhys ,1993, 20:103348.

[11]Muller KR and Mika Setal, An i ntroduction to

kernel-based learning algorithms , IEEE Trans

Neural Networks , 2001, 12(2):181202.

[12] Pham DL and PrinceJL , An adaptive fuz zyC-

meansalgorithm for image segmentation in the

presence of intensity inhomogeneities , Pattern

RecognitL ett , 1999, 20(1):5768.

26

Documents

43.an Image Segmentation Algorithm Based on Fuzzy c Means Clustering