Image Segmentation by Fuzzy Clustering: Methods and Issues

Sutton, Melanie; Bezdek, James C.; Cahoon, Tobias C.

doi:10.1016/b978-012077790-7/50010-2

Cited by 19 publications

(33 citation statements)

References 35 publications

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“…In addition, more complicated methods such as those described in 27,11 would be helpful in increasing the computational efficiency. Further performance improvement could be obtained by considering the grand mean and cluster scatter matrices within and between clusters in the unsupervised clustering 45,19,8,9,12,5,6,28,29,39,52.…”

Section: Resultsmentioning

confidence: 99%

“…The fundamentals of fuzzy clustering in medical image segmentation have been reviewed by Sutton et al 45. The main formalism presented in this paper is based on the grouped coordinate descent method (also called alternating optimization (AO)) 45.…”

Section: Introductionmentioning

confidence: 99%

“…The main formalism presented in this paper is based on the grouped coordinate descent method (also called alternating optimization (AO)) 45. Important details, such as singularity, initialization, rules of thumb for the parameters, methods to determine the number of clusters are not described in this paper due to space limitation, but can be found elsewhere 23,42.…”

Section: Introductionmentioning

confidence: 99%

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Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation

He¹,

Sajja²,

Datta³

et al. 2008

Ann Biomed Eng

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Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy cmeans (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation

He¹,

Sajja²,

Datta³

et al. 2008

Ann Biomed Eng

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“…The partition of the feature space (e.g., scatter plot) is through a crisp membership function, i.e., 0 or 1. The fuzzy clustering is a more sophisticated approach [7], which groups the pixels according to the degree of similarity between 0 and 1. This way of partition is more realistic in labeling the regions of foreground spots from the background as well as from possible artifacts.…”

Section: Clustering Based Approachesmentioning

confidence: 99%

“…Euclidian distance is another metric that is mostly used and expressed as (4) In this case, the minimization of objective function (2) leads to the k-means clustering. It is also called the hard c-means (HCM) clustering [7], where the membership element u ji is 1 if the jth data point x j belongs to class Ω i , and 0 otherwise, i.e., (5) The feature space is then partitioned by a membership matrix (U) of dimension n × c with the above element of u ji and each pixel is labeled by the element u ji .…”

Section: Fuzzy Clustering Modelsmentioning

confidence: 99%

A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression

Wang

Gunampally

Chen³

et al. 2007

J Sign Process Syst Sign Image

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Despite the widespread application of microarray imaging for biomedical imaging research, barriers still exist regarding its reliability for clinical use. A critical major problem lies in accurate spot segmentation and the quantification of gene expression level (mRNA) from the microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots with complex shapes such as donuts and scratches. Clustering approaches such as k-means and mixture models were introduced to overcome this difficulty, which use the hard labeling of each pixel. In this paper, we apply fuzzy clustering approaches for spot segmentation, which provides soft labeling of the pixel. We compare several fuzzy clustering approaches for microarray analysis and provide a comprehensive study of these approaches for spot segmentation. We show that possiblistic c-means clustering (PCM) provides the best performance in terms of stability criterion when testing on both a variety of simulated and real microarray images. In addition, we compared three statistical criteria in measuring gene expression levels and show that a new asymptotically unbiased statistic is able to quantify the gene expression level more accurately.

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