A Fuzzy Clustering Technique for Medical Image Segmentation

Tabakov, Martin

doi:10.1109/isefs.2006.251140

Cited by 17 publications

(8 citation statements)

References 4 publications

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“…The fuzzy cmeans algorithm is one of the most widely used fuzzy clustering methods [32,33]. Different fuzzy clustering methods have been applied on normal or fuzzy data to produce fuzzy clusters [34,35]. A major difference between the clustering problem studied in this paper and fuzzy clustering is that we focus on hard clustering, for which each object belongs to exactly one cluster.…”

Section: Related Workmentioning

confidence: 99%

Metric and trigonometric pruning for clustering of uncertain data in 2D geometric space

Ngai¹,

Kao²,

Cheng³

et al. 2011

Information Systems

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Section: Related Workmentioning

confidence: 99%

Metric and trigonometric pruning for clustering of uncertain data in 2D geometric space

Ngai¹,

Kao²,

Cheng³

et al. 2011

Information Systems

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“…The fuzzy c-means algorithm is one of the most widely used fuzzy clustering methods [24], [25]. Different fuzzy clustering methods have been applied on normal or fuzzy data to produce fuzzy clusters [26], [27]. A major difference between the clustering problem studied in this paper and fuzzy clustering is that we focus on hard clustering, for which each object belongs to exactly one cluster.…”

Section: Related Workmentioning

confidence: 99%

Clustering Uncertain Data Using Voronoi Diagrams

Kao

Lee

Cheung

et al. 2008

2008 Eighth IEEE International Conference on Data Mining

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Abstract-We study the problem of clustering uncertain objects whose locations are described by probability density functions (pdfs). We show that the UK-means algorithm, which generalizes the k-means algorithm to handle uncertain objects, is very inefficient. The inefficiency comes from the fact that UK-means computes expected distances (EDs) between objects and cluster representatives. For arbitrary pdfs, expected distances are computed by numerical integrations, which are costly operations. We propose pruning techniques that are based on Voronoi diagrams to reduce the number of expected distance calculations. These techniques are analytically proven to be more effective than the basic bounding-box-based technique previously known in the literature. We then introduce an R-tree index to organize the uncertain objects so as to reduce pruning overheads. We conduct experiments to evaluate the effectiveness of our novel techniques. We show that our techniques are additive and, when used in combination, significantly outperform previously known methods.

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“…In [9] a robust fuzzy clustering based segmentation method for noisy images is developed by introducing a novel penalty term into the objective function. In [25] the modified Fuzzy C-Means (FCM) algorithm is proposed to compensate for intensity non-homogeneities by modifying the objective function. Fast Generalized Fuzzy C-means clustering algorithm (FGFCM) for incorporating local spatial and gray information based on actual gray levels present in an image has been proposed in [24].…”

Section: Introductionmentioning

confidence: 99%

“…Few recent references applied them on colour images of CT SCAN [25] and MRI [26], in the medical imaging successfully. The modified FCM algorithm [7] mentioned above has been successfully applied on remote sensing images as well as synthetic images.…”

Section: Introductionmentioning

confidence: 99%

“…The classical FCM algorithm requires that the desired number of clusters be given in advance. This can be a problem when the clustering problem does not specify any desired number of clusters [25]. In this regard, we have proposed a novel scheme for initialization of FCM clustering which provides this information using colour similarity approach based on human perception discussed in section 2 and section 4.…”

Section: Introductionmentioning

confidence: 99%

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Color Image Segmentation using Fast Fuzzy C-Means Algorithm

Bhoyar

Kakde²

2010

ELCVIA

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This paper proposes modified FCM (Fuzzy C-Means) approach to colour image segmentation using JND (Just Noticeable Difference) histogram. Histogram of the given colour image is computed using JND colour model. This samples the colour space so that just enough number of histogram bins are obtained without compromising the visual image content. The number of histogram bins are further reduced using agglomeration. This agglomerated histogram yields the estimation of number of clusters, cluster seeds and the initial fuzzy partition for FCM algorithm. This is a novell approach to estimate the input parameters for FCM algorithm. The proposed fast FCM(FFCM) algorithm works on histogram bins as data elements instead of individual pixels. This significantly reduces the time complexity of FCM algorithm. To verify the effectiveness of the proposed image segmentation approach, its performance is evaluated on Berkeley Segmentation Database(BSD). Two significant criteria namely PSNR(Peak Signal to Noise Ratio) and PRI (Probabilistic Rand Index) are used to evaluate the performance. Although results show that the proposed algorithm applied to the JND histogram bins converges much faster and also gives better results than conventional FCM algorithm, in terms of PSNR and PRI.Key Words: Colour Image Segmentation, JND Histogram, Fuzzy C-means Clustering, Fast FCM IntroductionSegmentation involves partitioning an image into a set of homogeneous and meaningful regions, such that the pixels in each partitioned region posses an identical set of properties. Fuzzy set theory has been extensively applied in the area of image segmentation. The concepts of fuzzy first order and second order statistic like fuzzy histogram and co-occurrence matrices have been presented in [1,2]. These measures have been demonstrated to yield excellent segmentation results for the images having bimodal or multimodal histograms. The fuzzy second order statistics characterize textured images in a better way than their corresponding hard counterparts [3]. A number of research reports indicate the superiority of fuzzy sets in segmenting an image over its crisp counterparts.Fuzzy approaches to pixel classification have found applications in problems where precise knowledge about the pattern classes is not available, large number of pattern samples are not available for statistical estimation of parameters and patterns have partial membership to different classes. In many real world images, the clusters are not disjoint and a simple pattern or a pixel in an image may belong to different clusters. For example, a particular pixel in the sky may belong partly to sky and partly to the cloudy-sky class. Fuzzy approaches to supervised pattern classification and clustering may be found in [4]. Clustering methods are considered as an unsupervised classification technique, where there is no need for prior

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A Fuzzy Clustering Technique for Medical Image Segmentation

Cited by 17 publications

References 4 publications

Metric and trigonometric pruning for clustering of uncertain data in 2D geometric space

Metric and trigonometric pruning for clustering of uncertain data in 2D geometric space

Clustering Uncertain Data Using Voronoi Diagrams

Color Image Segmentation using Fast Fuzzy C-Means Algorithm

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