Multivariate image segmentation based on geometrically guided fuzzy C‐means clustering

Noordam, J.C.; Broek, W.H.A.M. van den

doi:10.1002/cem.656

Cited by 29 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method is also expensive in terms of computational time. Many researchers subsequently modified the objective functions and developed several robust FCM variants for image segmentation [12][13][14][15][16][17][18][19]. These algorithms were shown to have better performance than the standard FCM algorithm.…”

Section: Q2mentioning

confidence: 97%

“…It can also generate local optimal solution due to poor initialization. In order to make the FCM algorithm more robust to noise and outliers for image segmentation, many modified fuzzy clustering approaches have been reported in the past [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Pedrycz [8] introduced a conditional fuzzy C-means-based clustering method guided by an auxiliary or conditional variable.…”

Section: Q2mentioning

confidence: 99%

See 1 more Smart Citation

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Adhikari¹,

Sing

Basu

et al. 2015

Applied Soft Computing

159

View full text Add to dashboard Cite

Section: Q2mentioning

confidence: 97%

Section: Q2mentioning

confidence: 99%

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Adhikari¹,

Sing

Basu

et al. 2015

Applied Soft Computing

159

View full text Add to dashboard Cite

“…This makes that each column in the partition matrix can be arranged to an image with same spatial dimensions as the original multivariate image. Such a rearranged column is called a partition image [7]. The matrix index k, which corresponds with object k in the partition matrix U and data matrix X, also corresponds with a position (row, col) in the partition image.…”

Section: Local Neighbourhoodmentioning

confidence: 99%

“…Geometrically guided conditional FCM (GGC-FCM) [7] is able to include a priori locally spatial information to classify spurious pixels or small-sized blobs into a reject class. However, in applications such as product inspection where each pixel must be assigned to a true product class, the use of a reject class is impractical.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised segmentation of predefined shapes in multivariate images

Noordam

Broek²,

Buydens

2003

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

Fuzzy C-means (FCM) is an unsupervised clustering technique that is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the spatially guided FCM (SG-FCM) algorithm is presented which segments multivariate images by incorporating both spatial and spectral information. Spatial information is described by a geometrical shape description and can vary from a local neighbourhood to a more extended shape model such as Hough circle detection. A modified FCM objective function uses the spatial information as described by the shape model. This results in a segmented image in which the construction of the cluster prototypes is influenced by spatial information. The performance of SG-FCM is compared with both FCM and the sequence of FCM and a majority filter. The SG-FCM segmented image shows more homogeneous regions and less spurious pixels.

show abstract

“…Fuzzy clustering, as a soft segmentation technique, has been extensively analized and effectively applied in image segmentation and clustering [10]- [19]. Among the fuzzy clustering techniques, fuzzy cmeans (FCM) algorithm [10] is the generally well-liked technique which is used in image segmentation due to its robust features for uncertainty and can keep much more information as compared to hard segmentation techniques [11]. While the standared FCM algorithm works fit on most noise-free images, it is very aware to noise and other imaging artifacts, because it does not consider any data about spatial background.…”

Section: Introductionmentioning

confidence: 99%

Two Different Multi-Kernels for Fuzzy C-means Algorithm for Medical Image Segmentation

Venu¹,

Anuradha²

2015

IJETT

View full text Add to dashboard Cite

Abstract-A new image segmentation using multi-hyperbolic and multi-Gaussian kernel based fuzzy c-means algorithm (MHMGFCM) is proposed for medical magnetic resonance image (MRI) segmentation. The integration of two hyperbolic tangent kernels and two Gaussian kernels are used in the proposed algorithm for clustering of images. The performance of the proposed algorithm is tested on OASIS-MRI image dataset. The performance is tested in terms of score, number of iterations (NI) and execution time (TM) under different Gaussian noises on OASIS-MRI dataset. The results after investigation, the proposed method shows a significant improvement as compared to other existing methods in terms of score, NI and TM under different Gaussian noises on OASIS-MRI dataset.Keywords-FCM, multi-hyperbolic tangent function, Segmentation, multi-Gaussian Kernal, fuzzy, multiple-kernal. I. INTRODUCTIONIn image processing and computer vision, medical image segmentation is an active research area [1]. The process of clustering the image into non-overlaped, consistent regions is called the image segmentation. These regions are identical with respect to some features like texture, color, shape, intensity etc. Based on the features, the process of segmentation is separated into four groups: clustering (intensity), thresholding (intensity), region extraction (color or texture) and edge detection (texture).In Clustering is a procedure for classifying patterns or objects in such a manner that samples of the same cluster are more comparable to one another than samples belonging to other clusters. There are two main clustering approachs: the hard clustering technique and the fuzzy clustering technique. MacQueen [8] has proposed the k-means clustering algorithm. The k-means is one of the hard clustering technique. The usual hard clustering techniues classify every point of the records set just to one cluster. As a effect, the results are often very crusty, i.e., in image clustering every pixel of the image goes to one cluster. However, in many real conditions, issues such as restricted spatial resolution, reduced contrast, partly cover intensities,

show abstract

Multivariate image segmentation based on geometrically guided fuzzy C‐means clustering

Cited by 29 publications

References 12 publications

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Unsupervised segmentation of predefined shapes in multivariate images

Two Different Multi-Kernels for Fuzzy C-means Algorithm for Medical Image Segmentation

Contact Info

Product

Resources

About