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Unsupervised image segmentation using automated fuzzy c-means
(2007-12-01)
Sahaphong, Supatra
;
Hiransakolwong, Nualsawat
An unsupervised fuzzy clustering technique, Fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. However, the conventional FCM algorithm must be estimated by expertise users to determine the cluster numbers. To overcome the limitation of FCM algorithm, an automated fuzzy c-mean (AFCM) algorithm is presented in this paper. The proposed algorithm initiates the first two centroids of clusters by a method based on Otsu algorithm and automatically determines the appropriate cluster number for image segmentation. The performance of the proposed technique has been tested with reference to conventional FCM. The experimental results demonstrate that AFCM can spontaneously estimate the appropriate number of clusters and its performance is faster convergence than the performance of the conventional FCM. © 2007 IEEE.
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Segmentation of magnetic resonance images using discrete curve evolution and fuzzy clustering
(2007-12-01)
Supot, Sookpotharom
;
Thanapong, Chaichana
;
Chuchart, Pintavirooj
;
Manas, Sangworasil
The region clustering of a Magnetic Resonance Imaging (MRI) image is more complicate than a Computed Topography (CT) image because a MRI image composes of three components such as T1-weighted, T2-weighted, and Proton Density (PD) in each layer. However, the MRI images provide more detail than the CT images. Therefore, we propose a technique of the region clustering of MRI image by using Fuzzy c-means (FCM). The fuzzy c-means algorithm is an iterative operation, that is very time-consuming and makes the algorithm impractical for using in image segmentation. To cope with this problem, the discrete curve evolution (DCE) technique is applied to find the actual cluster center to refine the initial value of the fuzzy c-means algorithm, which reduces the convergence time. In experimental results, the proposed technique provides the same segmentation accuracy as the fuzzy c-means technique. Moreover, this technique takes lower computational time comparing to the previous method. © 2007 IEEE.
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Local area histogram equalization based multispectral image enhancement from clustering using competitive hopfield neural network
(2003-10-01)
Chitwong, S.
;
Boonmee, T.
;
Cheevasuvit, F.
One of important issues for enhancing image based on local area histogram equalization (LHE) is a clustering or segmenting technique. That is, the more the accuracy of separating image into specified classes is needed, the better the performance of enhancement is. As mentioned objective, in this paper, the competitive Hopfield neural network (CHNN) is then proposed for clustering to the LHE based image enhancement. By using simulated image, standard image and mutispectral image from Landsat 7 satellite, experimental results are shown in both accuracy of clustering and variance of the enhanced image. The criteria for a good enhancement algorithm is that it can give high variance in detail area, low variance in smooth and edge areas. Also comparing the variance of the enhanced image by both LHE and global area histogram equalization (GHE) methods shows that one from LHE outperforms. In addition, the enlarged image from small area is shown clearly by visualization. All results compare with the conventional methods such as fuzzy c-means (FCM).
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Enhancement of color image obtained from PCA-FCM technique using local area histogram equalization
(2002-12-01)
Chitwong, S.
;
Boonmee, T.
;
Cheevasuvit, F.
This paper presents local area enhancement of the segmented color image obtained from the multi-spectral image clustering by using FCM (fuzzy c-means). In case, the multi-spectral images, which have the number of bands more than that of 3, must decrease the data volume to remain the number of bands of 3 in order to correspond with the meaning of red, green, and blue images. PCA (Principal Components Analysis) is then used to transform original multi-spectral images into PCA images. The first three components having information more than that of original images of 95% is assigned as red, green, and blue images, namely RGB color image. FCM clustering apply to RGB color image, separately. This method is called the PCA-FCM technique being the multi-spectral image clustering. By applying such technique, the result images consisted of red, green, and blue images separately are the segmented images. By histogram equalization algorithm, the result of local area enhancement based on a number of clusters as the segmented image can solve effect of intensity saturation from global area enhancement and the perceptibility of color image is clearly improved.
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Intra/inter-band coding in FCM-VQ of multispectral images
(2001-12-01)
Rangsanseri, Yuttapong
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Sangthongpraow, Uthai
;
Thitimajshima, Punya
This paper presents a multispectral image compression method based on vector quantization technique that uses fuzzy c-means (FCM) algorithm to generate the codebook. There are two ways to form the vector set from the input image: the intraband vector forming where each vector was formed by dividing the input image into blocks, and the interband vector forming where each vector was formed by the gray values of all bands representing a pixel. In this research, a modified version of FCM was used to reduce the execution time. The experimental results comparing the effect of both vector forming methods are given.

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