International Journal of Fuzzy System Applications, 3(4), 47-59, October-December 2013 47

Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

ABSTRACTAlthough fuzzy c-means (FCM) algorithm and some of its variants have been extensively widely used in unsupervised medical image segmentation applications in recent years, they more or less suffer from either noise sensitivity or loss of details, which always is a key point to medical image processing. This paper pres-ents a novel FCM variation method that is suitable for medical image segmentation. The proposed method, typically by incorporating multi-resolution bilateral filter which is combined with wavelet thresholding, provides the following advantages: (1) it is less sensitive to both high- and low-frequency noise and removes spurious blobs and noisy spots, (2) it yields more homogeneous clustering regions, and (3) it preserves detail, thus significantly improving clustering performance. By the use of synthetic and multiple-feature magnetic resonance (MR) image data, the experimental results and quantitative analyses suggest that, compared to other fuzzy clustering algorithms, the proposed method further enhances the robustness to noisy images and capacity of detail preservation.

Fuzzy Clustering with Multi-Resolution Bilateral Filtering for

Medical Image SegmentationKai Xiao, School of Software, Shanghai Jiaotong University, Shanghai, China

Jianli Li, School of Software, Shanghai Jiaotong University, Shanghai, China

Shuangjiu Xiao, School of Software, Shanghai Jiaotong University, Shanghai, China

Haibing Guan, Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science, Shanghai Jiaotong University, Shanghai, China

Fang Fang, Shanghai First People’s Hospital, Shanghai, China

Aboul Ella Hassanien, Department of Information Technology, Cairo University, Giza, Cairo

Keywords: Bilateral Filtering, Fuzzy C-Means (FCM), Medical Image Segmentation, Multi-Resolution, Wavelet

DOI: 10.4018/ijfsa.2013100104

Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

48 International Journal of Fuzzy System Applications, 3(4), 47-59, October-December 2013

1. INTRODUCTION

Medical imaging is the technique and process used to create images of the human body for clinical purposes or study of normal anatomy and physiology. In recent years, image process-ing has become a more and more important analysis tool in medical science.

Image segmentation, often as an initial part of image processing, which usually is a preprocessing step in many image, video and computer vision applications, always plays a key role in medical image processing. It is the process of dividing an image into multiple parts which is of interest. This is typically used to identify objects or other relevant informa-tion in digital images that includes computed tomographic (CT), magnetic resonance imaging (MRI), and ultrasound etc.

There are many different ways to perform image segmentation, including thresholding methods, clustering methods, transform meth-ods, and texture methods. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is usually still difficult to assess whether one al-gorithm produces more accurate segmentations than another, especially in medical imaging.

The term “fuzzy logic” was introduced with the 1965 proposal of fuzzy set theory by Zadeh (1965). With rapid growth, researchers find it is suitable and useful to be used in image processing, especially in medical image, for its feature of uncertainty, ambiguity and vagueness.

Clustering can be considered the most important unsupervised learning problem. In fuzzy clustering, each point has a degree of belonging to clusters, as in fuzzy set theory, rather than belonging completely to just one cluster. Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters and has been a very important tool for image processing in clustering objects in an image.

Fuzzy c-means (FCM) (Dunn, 1974; Bezdek, 1980; Bezdek, 1981) is an unsupervised pattern recognition algorithm that has been widely applied to feature analysis, clustering,

and classifier designs in various fields. In the last decades, one successful application of FCM is image segmentation where an image can be represented in various feature space, and FCM algorithm separates the image data by grouping similar data points in the feature space into clusters (Bezdek et al. 1993; Clarke et al. 1995; Pham et al. 2000). The algorithm iteratively minimizes a cost function which is dependent on the pixels to the cluster centers in the feature domain.

Although fuzzy c-means (FCM) algorithm and some of its variants have been extensively widely used in unsupervised medical image segmentation applications in recent years, they more or less suffer from either noise sensitivity or loss of details, which always is a key point to medical image processing. This paper presents a novel FCM variation method that is suitable for medical image segmentation.

We will discuss related work about current existed FCM methods in next section.

2. RELATED WORK

Image segmentation is an important process-ing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images (Hui et al. 2008).

Generally, the pixels on an image are cor-related at certain degree, i.e. the pixels in the immediate neighbors possess relatively similar feature data. In other words, normally the prob-ability that adjacent pixels belong to the same cluster will be high. This is especially true in medical images. Therefore, taking advantage of this spatial information can assist medical image segmentation.

Since the conventional FCM algorithm does not fully utilize this information, research-ers have proposed various strategies to allow

11 more pages are available in the full version of this

document, which may be purchased using the "Add to Cart"

button on the product's webpage:

www.igi-global.com/article/fuzzy-clustering-with-multi-

resolution-bilateral-filtering-for-medical-image-

segmentation/101769?camid=4v1

This title is available in InfoSci-Journals, InfoSci-Journal

Disciplines Computer Science, Security, and Information

Technology. Recommend this product to your librarian:

www.igi-global.com/e-resources/library-

recommendation/?id=2

Related Content

An Intelligent and Dynamic Decision Support System for Nonlinear

EnvironmentsS. Uma and J. Suganthi (2012). International Journal of Intelligent Information

Technologies (pp. 43-61).

www.igi-global.com/article/intelligent-dynamic-decision-support-system/74829?camid=4v1a

OntoClippy: A User-Friendly Ontology Design and Creation MethodologyNikolai Dahlem (2011). International Journal of Intelligent Information Technologies

(pp. 15-32).

www.igi-global.com/article/ontoclippy-user-friendly-ontology-design/50483?camid=4v1a

Towards a Design Process for Integrating Product Recommendation

Services in E-MarketsNikos Manouselis and Constantina Costopoulou (2008). Intelligent Information

Technologies: Concepts, Methodologies, Tools, and Applications (pp. 2365-2382).

www.igi-global.com/chapter/towards-design-process-integrating-product/24408?camid=4v1a

On Dependability Issues in Ambient Intelligence SystemsMarcello Cinque, Antonio Coronato and Alessandro Testa (2013). Pervasive and

Ubiquitous Technology Innovations for Ambient Intelligence Environments (pp. 146-

155).

www.igi-global.com/chapter/dependability-issues-ambient-intelligence-systems/68932?camid=4v1a